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Simulation
Simulation
from Wikipedia

A simulation is an imitative representation of a process or system that could exist in the real world.[1][2][3] In this broad sense, simulation can often be used interchangeably with model.[2] Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time.[3] Another way to distinguish between the terms is to define simulation as experimentation with the help of a model.[4] This definition includes time-independent simulations. Often, computers are used to execute the simulation.

Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games. Simulation is also used with scientific modelling of natural systems or human systems to gain insight into their functioning,[5] as in economics. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Simulation is also used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.[6]

Key issues in modeling and simulation include the acquisition of valid sources of information about the relevant selection of key characteristics and behaviors used to build the model, the use of simplifying approximations and assumptions within the model, and fidelity and validity of the simulation outcomes. Procedures and protocols for model verification and validation are an ongoing field of academic study, refinement, research and development in simulations technology or practice, particularly in the work of computer simulation.

Classification and terminology

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Human-in-the-loop simulation of outer space
Visualization of a direct numerical simulation model

Historically, simulations used in different fields developed largely independently, but 20th-century studies of systems theory and cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept.

Physical simulation refers to simulation in which physical objects are substituted for the real thing. These physical objects are often chosen because they are smaller or cheaper than the actual object or system. (See also: physical model and scale model.) Alternatively, physical simulation may refer to computer simulations considering selected laws of physics, as in multiphysics simulation.[7] (See also: Physics engine.)

Interactive simulation is a special kind of physical simulation, often referred to as a human-in-the-loop simulation, in which physical simulations include human operators, such as in a flight simulator, sailing simulator, or driving simulator.

Continuous simulation is a simulation based on continuous-time rather than discrete-time steps, using numerical integration of differential equations.[8]

Discrete-event simulation studies systems whose states change their values only at discrete times.[9] For example, a simulation of an epidemic could change the number of infected people at time instants when susceptible individuals get infected or when infected individuals recover.

Stochastic simulation is a simulation where some variable or process is subject to random variations and is projected using Monte Carlo techniques using pseudo-random numbers. Thus replicated runs with the same boundary conditions will each produce different results within a specific confidence band.[8]

Deterministic simulation is a simulation which is not stochastic: thus the variables are regulated by deterministic algorithms. So replicated runs from the same boundary conditions always produce identical results.

Hybrid simulation (or combined simulation) corresponds to a mix between continuous and discrete event simulation and results in integrating numerically the differential equations between two sequential events to reduce the number of discontinuities.[10]

A stand-alone simulation is a simulation running on a single workstation by itself.

A distributed simulation is one which uses more than one computer simultaneously, to guarantee access from/to different resources (e.g. multi-users operating different systems, or distributed data sets); a classical example is Distributed Interactive Simulation (DIS).[11]

Parallel simulation speeds up a simulation's execution by concurrently distributing its workload over multiple processors, as in high-performance computing.[12]

Interoperable simulation is where multiple models, simulators (often defined as federates) interoperate locally, distributed over a network; a classical example is High-Level Architecture.[13][14]

Modeling and simulation as a service is where simulation is accessed as a service over the web.[15]

Modeling, interoperable simulation and serious games is where serious game approaches (e.g. game engines and engagement methods) are integrated with interoperable simulation.[16]

Simulation fidelity is used to describe the accuracy of a simulation and how closely it imitates the real-life counterpart. Fidelity is broadly classified as one of three categories: low, medium, and high. Specific descriptions of fidelity levels are subject to interpretation, but the following generalizations can be made:

  • Low – the minimum simulation required for a system to respond to accept inputs and provide outputs
  • Medium – responds automatically to stimuli, with limited accuracy
  • High – nearly indistinguishable or as close as possible to the real system

A synthetic environment is a computer simulation that can be included in human-in-the-loop simulations.[19]

Simulation in failure analysis refers to simulation in which we create environment/conditions to identify the cause of equipment failure. This can be the best and fastest method to identify the failure cause.

Computer simulation

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A computer simulation (or "sim") is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables in the simulation, predictions may be made about the behaviour of the system. It is a tool to virtually investigate the behaviour of the system under study.[3]

Computer simulation has become a useful part of modeling many natural systems in physics, chemistry and biology,[20] and human systems in economics and social science (e.g., computational sociology) as well as in engineering to gain insight into the operation of those systems. A good example of the usefulness of using computers to simulate can be found in the field of network traffic simulation. In such simulations, the model behaviour will change each simulation according to the set of initial parameters assumed for the environment.

Traditionally, the formal modeling of systems has been via a mathematical model, which attempts to find analytical solutions enabling the prediction of the behaviour of the system from a set of parameters and initial conditions. Computer simulation is often used as an adjunct to, or substitution for, modeling systems for which simple closed form analytic solutions are not possible. There are many different types of computer simulation, the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states would be prohibitive or impossible.

Several software packages exist for running computer-based simulation modeling (e.g. Monte Carlo simulation, stochastic modeling, multimethod modeling) that makes all the modeling almost effortless.

Modern usage of the term "computer simulation" may encompass virtually any computer-based representation.

Computer science

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In computer science, simulation has some specialized meanings: Alan Turing used the term simulation to refer to what happens when a universal machine executes a state transition table (in modern terminology, a computer runs a program) that describes the state transitions, inputs and outputs of a subject discrete-state machine.[21] The computer simulates the subject machine. Accordingly, in theoretical computer science the term simulation is a relation between state transition systems, useful in the study of operational semantics.

Less theoretically, an interesting application of computer simulation is to simulate computers using computers. In computer architecture, a type of simulator, typically called an emulator, is often used to execute a program that has to run on some inconvenient type of computer (for example, a newly designed computer that has not yet been built or an obsolete computer that is no longer available), or in a tightly controlled testing environment (see Computer architecture simulator and Platform virtualization). For example, simulators have been used to debug a microprogram or sometimes commercial application programs, before the program is downloaded to the target machine. Since the operation of the computer is simulated, all of the information about the computer's operation is directly available to the programmer, and the speed and execution of the simulation can be varied at will.

Simulators may also be used to interpret fault trees, or test VLSI logic designs before they are constructed. Symbolic simulation uses variables to stand for unknown values.

In the field of optimization, simulations of physical processes are often used in conjunction with evolutionary computation to optimize control strategies.

Simulation in education and training

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Military simulators pdf

Simulation is extensively used for educational purposes. It is used for cases where it is prohibitively expensive or simply too dangerous to allow trainees to use the real equipment in the real world. In such situations they will spend time learning valuable lessons in a "safe" virtual environment yet living a lifelike experience (or at least it is the goal). Often the convenience is to permit mistakes during training for a safety-critical system.

Simulations in education are somewhat like training simulations. They focus on specific tasks. The term 'microworld' is used to refer to educational simulations which model some abstract concept rather than simulating a realistic object or environment, or in some cases model a real-world environment in a simplistic way so as to help a learner develop an understanding of the key concepts. Normally, a user can create some sort of construction within the microworld that will behave in a way consistent with the concepts being modeled. Seymour Papert was one of the first to advocate the value of microworlds, and the Logo programming environment developed by Papert is one of the most well-known microworlds.

Project management simulation is increasingly used to train students and professionals in the art and science of project management. Using simulation for project management training improves learning retention and enhances the learning process.[22][23]

Social simulations may be used in social science classrooms to illustrate social and political processes in anthropology, economics, history, political science, or sociology courses, typically at the high school or university level. These may, for example, take the form of civics simulations, in which participants assume roles in a simulated society, or international relations simulations in which participants engage in negotiations, alliance formation, trade, diplomacy, and the use of force. Such simulations might be based on fictitious political systems, or be based on current or historical events. An example of the latter would be Barnard College's Reacting to the Past series of historical educational games.[24] The National Science Foundation has also supported the creation of reacting games that address science and math education.[25] In social media simulations, participants train communication with critics and other stakeholders in a private environment.

In recent years, there has been increasing use of social simulations for staff training in aid and development agencies. The Carana simulation, for example, was first developed by the United Nations Development Programme, and is now used in a very revised form by the World Bank for training staff to deal with fragile and conflict-affected countries.[26]

Military uses for simulation often involve aircraft or armoured fighting vehicles, but can also target small arms and other weapon systems training. Specifically, virtual firearms ranges have become the norm in most military training processes and there is a significant amount of data to suggest this is a useful tool for armed professionals.[27]

Virtual simulation

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A virtual simulation is a category of simulation that uses simulation equipment to create a simulated world for the user. Virtual simulations allow users to interact with a virtual world. Virtual worlds operate on platforms of integrated software and hardware components. In this manner, the system can accept input from the user (e.g., body tracking, voice/sound recognition, physical controllers) and produce output to the user (e.g., visual display, aural display, haptic display) .[28] Virtual simulations use the aforementioned modes of interaction to produce a sense of immersion for the user.

Virtual simulation input hardware

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Motorcycle simulator of Bienal do Automóvel exhibition, in Belo Horizonte, Brazil

There is a wide variety of input hardware available to accept user input for virtual simulations. The following list briefly describes several of them:

  • Body tracking: The motion capture method is often used to record the user's movements and translate the captured data into inputs for the virtual simulation. For example, if a user physically turns their head, the motion would be captured by the simulation hardware in some way and translated to a corresponding shift in view within the simulation.
    • Capture suits and/or gloves may be used to capture movements of users body parts. The systems may have sensors incorporated inside them to sense movements of different body parts (e.g., fingers). Alternatively, these systems may have exterior tracking devices or marks that can be detected by external ultrasound, optical receivers or electromagnetic sensors. Internal inertial sensors are also available on some systems. The units may transmit data either wirelessly or through cables.
    • Eye trackers can also be used to detect eye movements so that the system can determine precisely where a user is looking at any given instant.
  • Physical controllers: Physical controllers provide input to the simulation only through direct manipulation by the user. In virtual simulations, tactile feedback from physical controllers is highly desirable in a number of simulation environments.
    • Omnidirectional treadmills can be used to capture the users locomotion as they walk or run.
    • High fidelity instrumentation such as instrument panels in virtual aircraft cockpits provides users with actual controls to raise the level of immersion. For example, pilots can use the actual global positioning system controls from the real device in a simulated cockpit to help them practice procedures with the actual device in the context of the integrated cockpit system.
  • Voice/sound recognition: This form of interaction may be used either to interact with agents within the simulation (e.g., virtual people) or to manipulate objects in the simulation (e.g., information). Voice interaction presumably increases the level of immersion for the user.
    • Users may use headsets with boom microphones, lapel microphones or the room may be equipped with strategically located microphones.

Current research into user input systems

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Research in future input systems holds a great deal of promise for virtual simulations. Systems such as brain–computer interfaces (BCIs) offer the ability to further increase the level of immersion for virtual simulation users. Lee, Keinrath, Scherer, Bischof, Pfurtscheller[29] proved that naïve subjects could be trained to use a BCI to navigate a virtual apartment with relative ease. Using the BCI, the authors found that subjects were able to freely navigate the virtual environment with relatively minimal effort. It is possible that these types of systems will become standard input modalities in future virtual simulation systems.

Virtual simulation output hardware

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There is a wide variety of output hardware available to deliver a stimulus to users in virtual simulations. The following list briefly describes several of them:

  • Visual display: Visual displays provide the visual stimulus to the user.
    • Stationary displays can vary from a conventional desktop display to 360-degree wrap-around screens to stereo three-dimensional screens. Conventional desktop displays can vary in size from 15 to 60 inches (380 to 1,520 mm). Wrap around screens is typically used in what is known as a cave automatic virtual environment (CAVE). Stereo three-dimensional screens produce three-dimensional images either with or without special glasses—depending on the design.
    • Head-mounted displays (HMDs) have small displays that are mounted on headgear worn by the user. These systems are connected directly into the virtual simulation to provide the user with a more immersive experience. Weight, update rates and field of view are some of the key variables that differentiate HMDs. Naturally, heavier HMDs are undesirable as they cause fatigue over time. If the update rate is too slow, the system is unable to update the displays fast enough to correspond with a quick head turn by the user. Slower update rates tend to cause simulation sickness and disrupt the sense of immersion. Field of view or the angular extent of the world that is seen at a given moment field of view can vary from system to system and has been found to affect the user's sense of immersion.
  • Aural display: Several different types of audio systems exist to help the user hear and localize sounds spatially. Special software can be used to produce 3D audio effects 3D audio to create the illusion that sound sources are placed within a defined three-dimensional space around the user.
    • Stationary conventional speaker systems may be used to provide dual or multi-channel surround sound. However, external speakers are not as effective as headphones in producing 3D audio effects.[28]
    • Conventional headphones offer a portable alternative to stationary speakers. They also have the added advantages of masking real-world noise and facilitate more effective 3D audio sound effects.[28] [dubiousdiscuss]
  • Haptic display: These displays provide a sense of touch to the user (haptic technology). This type of output is sometimes referred to as force feedback.
    • Tactile tile displays use different types of actuators such as inflatable bladders, vibrators, low-frequency sub-woofers, pin actuators and/or thermo-actuators to produce sensations for the user.
    • End effector displays can respond to users inputs with resistance and force.[28] These systems are often used in medical applications for remote surgeries that employ robotic instruments.[30]
  • Vestibular display: These displays provide a sense of motion to the user (motion simulator). They often manifest as motion bases for virtual vehicle simulation such as driving simulators or flight simulators. Motion bases are fixed in place but use actuators to move the simulator in ways that can produce the sensations pitching, yawing or rolling. The simulators can also move in such a way as to produce a sense of acceleration on all axes (e.g., the motion base can produce the sensation of falling).

Clinical healthcare simulators

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Clinical healthcare simulators are increasingly being developed and deployed to teach therapeutic and diagnostic procedures as well as medical concepts and decision making to personnel in the health professions. Simulators have been developed for training procedures ranging from the basics such as blood draw, to laparoscopic surgery[31] and trauma care. They are also important to help on prototyping new devices[32] for biomedical engineering problems. Currently, simulators are applied to research and develop tools for new therapies,[33] treatments[34] and early diagnosis[35] in medicine.

Many medical simulators involve a computer connected to a plastic simulation of the relevant anatomy.[36] Sophisticated simulators of this type employ a life-size mannequin that responds to injected drugs and can be programmed to create simulations of life-threatening emergencies.

In other simulations, visual components of the procedure are reproduced by computer graphics techniques, while touch-based components are reproduced by haptic feedback devices combined with physical simulation routines computed in response to the user's actions. Medical simulations of this sort will often use 3D CT or MRI scans of patient data to enhance realism. Some medical simulations are developed to be widely distributed (such as web-enabled simulations[37] and procedural simulations[38] that can be viewed via standard web browsers) and can be interacted with using standard computer interfaces, such as the keyboard and mouse.

Placebo

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An important medical application of a simulator—although, perhaps, denoting a slightly different meaning of simulator—is the use of a placebo drug, a formulation that simulates the active drug in trials of drug efficacy.

Improving patient safety

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Patient safety is a concern in the medical industry. Patients have been known to suffer injuries and even death due to management error, and lack of using best standards of care and training. According to Building a National Agenda for Simulation-Based Medical Education (Eder-Van Hook, Jackie, 2004), "a health care provider's ability to react prudently in an unexpected situation is one of the most critical factors in creating a positive outcome in medical emergency, regardless of whether it occurs on the battlefield, freeway, or hospital emergency room." Eder-Van Hook (2004) also noted that medical errors kill up to 98,000 with an estimated cost between $37 and $50 million and $17 to $29 billion for preventable adverse events dollars per year.

Simulation is being used to study patient safety, as well as train medical professionals.[39] Studying patient safety and safety interventions in healthcare is challenging, because there is a lack of experimental control (i.e., patient complexity, system/process variances) to see if an intervention made a meaningful difference (Groves & Manges, 2017).[40] An example of innovative simulation to study patient safety is from nursing research. Groves et al. (2016) used a high-fidelity simulation to examine nursing safety-oriented behaviors during times such as change-of-shift report.[39]

However, the value of simulation interventions to translating to clinical practice are is still debatable.[41] As Nishisaki states, "there is good evidence that simulation training improves provider and team self-efficacy and competence on manikins. There is also good evidence that procedural simulation improves actual operational performance in clinical settings."[41] However, there is a need to have improved evidence to show that crew resource management training through simulation.[41] One of the largest challenges is showing that team simulation improves team operational performance at the bedside.[42] Although evidence that simulation-based training actually improves patient outcome has been slow to accrue, today the ability of simulation to provide hands-on experience that translates to the operating room is no longer in doubt.[43][44][45]

One of the largest factors that might impact the ability to have training impact the work of practitioners at the bedside is the ability to empower frontline staff (Stewart, Manges, Ward, 2015).[42][46] Another example of an attempt to improve patient safety through the use of simulations training is patient care to deliver just-in-time service or/and just-in-place. This training consists of 20  minutes of simulated training just before workers report to shift. One study found that just in time training improved the transition to the bedside. The conclusion as reported in Nishisaki (2008) work, was that the simulation training improved resident participation in real cases; but did not sacrifice the quality of service. It could be therefore hypothesized that by increasing the number of highly trained residents through the use of simulation training, that the simulation training does, in fact, increase patient safety.

History of simulation in healthcare

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The first medical simulators were simple models of human patients.[47]

Since antiquity, these representations in clay and stone were used to demonstrate clinical features of disease states and their effects on humans. Models have been found in many cultures and continents. These models have been used in some cultures (e.g., Chinese culture) as a "diagnostic" instrument, allowing women to consult male physicians while maintaining social laws of modesty. Models are used today to help students learn the anatomy of the musculoskeletal system and organ systems.[47]

In 2002, the Society for Simulation in Healthcare (SSH) was formed to become a leader in international interprofessional advances the application of medical simulation in healthcare[48]

The need for a "uniform mechanism to educate, evaluate, and certify simulation instructors for the health care profession" was recognized by McGaghie et al. in their critical review of simulation-based medical education research.[49] In 2012 the SSH piloted two new certifications to provide recognition to educators in an effort to meet this need.[50]

Type of models

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Active models

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Active models that attempt to reproduce living anatomy or physiology are recent developments. The famous "Harvey" mannequin was developed at the University of Miami and is able to recreate many of the physical findings of the cardiology examination, including palpation, auscultation, and electrocardiography.[51]

Interactive models

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More recently, interactive models have been developed that respond to actions taken by a student or physician.[51] Until recently, these simulations were two dimensional computer programs that acted more like a textbook than a patient. Computer simulations have the advantage of allowing a student to make judgments, and also to make errors. The process of iterative learning through assessment, evaluation, decision making, and error correction creates a much stronger learning environment than passive instruction.

Computer simulators

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3DiTeams learner is percussing the patient's chest in virtual field hospital.

Simulators have been proposed as an ideal tool for assessment of students for clinical skills.[52] For patients, "cybertherapy" can be used for sessions simulating traumatic experiences, from fear of heights to social anxiety.[53]

Programmed patients and simulated clinical situations, including mock disaster drills, have been used extensively for education and evaluation. These "lifelike" simulations are expensive, and lack reproducibility. A fully functional "3Di" simulator would be the most specific tool available for teaching and measurement of clinical skills. Gaming platforms have been applied to create these virtual medical environments to create an interactive method for learning and application of information in a clinical context.[54][55]

Immersive disease state simulations allow a doctor or HCP to experience what a disease actually feels like. Using sensors and transducers symptomatic effects can be delivered to a participant allowing them to experience the patients disease state.

Such a simulator meets the goals of an objective and standardized examination for clinical competence.[56] This system is superior to examinations that use "standard patients" because it permits the quantitative measurement of competence, as well as reproducing the same objective findings.[57]

Simulation in entertainment

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Simulation in entertainment encompasses many large and popular industries such as film, television, video games (including serious games) and rides in theme parks. Although modern simulation is thought to have its roots in training and the military, in the 20th century it also became a conduit for enterprises which were more hedonistic in nature.

History of visual simulation in film and games

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Early history (1940s and 1950s)

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The first simulation game may have been created as early as 1947 by Thomas T. Goldsmith Jr. and Estle Ray Mann. This was a straightforward game that simulated a missile being fired at a target. The curve of the missile and its speed could be adjusted using several knobs. In 1958, a computer game called Tennis for Two was created by Willy Higginbotham which simulated a tennis game between two players who could both play at the same time using hand controls and was displayed on an oscilloscope.[58] This was one of the first electronic video games to use a graphical display.

1970s and early 1980s

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Computer-generated imagery was used in the film to simulate objects as early as 1972 in A Computer Animated Hand, parts of which were shown on the big screen in the 1976 film Futureworld. This was followed by the "targeting computer" that young Skywalker turns off in the 1977 film Star Wars.

The film Tron (1982) was the first film to use computer-generated imagery for more than a couple of minutes.[59]

Advances in technology in the 1980s caused 3D simulation to become more widely used and it began to appear in movies and in computer-based games such as Atari's Battlezone (1980) and Acornsoft's Elite (1984), one of the first wire-frame 3D graphics games for home computers.

Pre-virtual cinematography era (early 1980s to 1990s)

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Advances in technology in the 1980s made the computer more affordable and more capable than they were in previous decades,[60] which facilitated the rise of computer such as the Xbox gaming. The first video game consoles released in the 1970s and early 1980s fell prey to the industry crash in 1983, but in 1985, Nintendo released the Nintendo Entertainment System (NES) which became one of the best selling consoles in video game history.[61] In the 1990s, computer games became widely popular with the release of such game as The Sims and Command & Conquer and the still increasing power of desktop computers. Today, computer simulation games such as World of Warcraft are played by millions of people around the world.

In 1993, the film Jurassic Park became the first popular film to use computer-generated graphics extensively, integrating the simulated dinosaurs almost seamlessly into live action scenes.

This event transformed the film industry; in 1995, the film Toy Story was the first film to use only computer-generated images and by the new millennium computer generated graphics were the leading choice for special effects in films.[62]

Virtual cinematography (early 2000s–present)

[edit]

The advent of virtual cinematography in the early 2000s has led to an explosion of movies that would have been impossible to shoot without it. Classic examples are the digital look-alikes of Neo, Smith and other characters in the Matrix sequels and the extensive use of physically impossible camera runs in The Lord of the Rings trilogy.

The terminal in the Pan Am (TV series) no longer existed during the filming of this 2011–2012 aired series, which was no problem as they created it in virtual cinematography using automated viewpoint finding and matching in conjunction with compositing real and simulated footage, which has been the bread and butter of the movie artist in and around film studios since the early 2000s.

Computer-generated imagery is "the application of the field of 3D computer graphics to special effects". This technology is used for visual effects because they are high in quality, controllable, and can create effects that would not be feasible using any other technology either because of cost, resources or safety.[63] Computer-generated graphics can be seen in many live-action movies today, especially those of the action genre. Further, computer-generated imagery has almost completely supplanted hand-drawn animation in children's movies which are increasingly computer-generated only. Examples of movies that use computer-generated imagery include Finding Nemo, 300 and Iron Man.

Examples of non-film entertainment simulation

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Simulation games

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Simulation games, as opposed to other genres of video and computer games, represent or simulate an environment accurately. Moreover, they represent the interactions between the playable characters and the environment realistically. These kinds of games are usually more complex in terms of gameplay.[64] Simulation games have become incredibly popular among people of all ages.[65] Popular simulation games include SimCity and Tiger Woods PGA Tour. There are also flight simulator and driving simulator games.

Theme park rides

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Simulators have been used for entertainment since the Link Trainer in the 1930s.[66] The first modern simulator ride to open at a theme park was Disney's Star Tours in 1987 soon followed by Universal's The Funtastic World of Hanna-Barbera in 1990 which was the first ride to be done entirely with computer graphics.[67]

Simulator rides are the progeny of military training simulators and commercial simulators, but they are different in a fundamental way. While military training simulators react realistically to the input of the trainee in real time, ride simulators only feel like they move realistically and move according to prerecorded motion scripts.[67] One of the first simulator rides, Star Tours, which cost $32 million, used a hydraulic motion based cabin. The movement was programmed by a joystick. Today's simulator rides, such as The Amazing Adventures of Spider-Man include elements to increase the amount of immersion experienced by the riders such as: 3D imagery, physical effects (spraying water or producing scents), and movement through an environment.[68]

Simulation and manufacturing

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Manufacturing simulation represents one of the most important applications of simulation. This technique represents a valuable tool used by engineers when evaluating the effect of capital investment in equipment and physical facilities like factory plants, warehouses, and distribution centers. Simulation can be used to predict the performance of an existing or planned system and to compare alternative solutions for a particular design problem.[69]

Another important goal of simulation in manufacturing systems is to quantify system performance. Common measures of system performance include the following:[70]

  • Throughput under average and peak loads
  • System cycle time (how long it takes to produce one part)
  • Use of resource, labor, and machines
  • Bottlenecks and choke points
  • Queuing at work locations
  • Queuing and delays caused by material-handling devices and systems
  • WIP storages needs
  • Staffing requirements
  • Effectiveness of scheduling systems
  • Effectiveness of control systems

More examples of simulation

[edit]

Automobiles

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An automobile simulator provides an opportunity to reproduce the characteristics of real vehicles in a virtual environment. It replicates the external factors and conditions with which a vehicle interacts enabling a driver to feel as if they are sitting in the cab of their own vehicle. Scenarios and events are replicated with sufficient reality to ensure that drivers become fully immersed in the experience rather than simply viewing it as an educational experience.

The simulator provides a constructive experience for the novice driver and enables more complex exercises to be undertaken by the more mature driver. For novice drivers, truck simulators provide an opportunity to begin their career by applying best practice. For mature drivers, simulation provides the ability to enhance good driving or to detect poor practice and to suggest the necessary steps for remedial action. For companies, it provides an opportunity to educate staff in the driving skills that achieve reduced maintenance costs, improved productivity and, most importantly, to ensure the safety of their actions in all possible situations.

Biomechanics

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A biomechanics simulator is a simulation platform for creating dynamic mechanical models built from combinations of rigid and deformable bodies, joints, constraints, and various force actuators. It is specialized for creating biomechanical models of human anatomical structures, with the intention to study their function and eventually assist in the design and planning of medical treatment.

A biomechanics simulator is used to analyze walking dynamics, study sports performance, simulate surgical procedures, analyze joint loads, design medical devices, and animate human and animal movement.

A neuromechanical simulator that combines biomechanical and biologically realistic neural network simulation. It allows the user to test hypotheses on the neural basis of behavior in a physically accurate 3-D virtual environment.

City and urban

[edit]

A city simulator can be a city-building game but can also be a tool used by urban planners to understand how cities are likely to evolve in response to various policy decisions. AnyLogic is an example of modern, large-scale urban simulators designed for use by urban planners. City simulators are generally agent-based simulations with explicit representations for land use and transportation. UrbanSim and LEAM are examples of large-scale urban simulation models that are used by metropolitan planning agencies and military bases for land use and transportation planning.

Christmas

[edit]

Several Christmas-themed simulations exist, many of which are centred around Santa Claus. An example of these simulations are websites which claim to allow the user to track Santa Claus. Due to the fact that Santa is a legendary character and not a real, living person, it is impossible to provide actual information on his location, and services such as NORAD Tracks Santa and the Google Santa Tracker (the former of which claims to use radar and other technologies to track Santa)[71] display fake, predetermined location information to users. Another example of these simulations are websites that claim to allow the user to email or send messages to Santa Claus. Websites such as emailSanta.com or Santa's former page on the now-defunct Windows Live Spaces by Microsoft use automated programs or scripts to generate personalized replies claimed to be from Santa himself based on user input.[72][73][74][75]

Classroom of the future

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The classroom of the future will probably contain several kinds of simulators, in addition to textual and visual learning tools. This will allow students to enter the clinical years better prepared, and with a higher skill level. The advanced student or postgraduate will have a more concise and comprehensive method of retraining—or of incorporating new clinical procedures into their skill set—and regulatory bodies and medical institutions will find it easier to assess the proficiency and competency of individuals.

The classroom of the future will also form the basis of a clinical skills unit for continuing education of medical personnel; and in the same way that the use of periodic flight training assists airline pilots, this technology will assist practitioners throughout their career.[citation needed]

The simulator will be more than a "living" textbook, it will become an integral a part of the practice of medicine.[citation needed] The simulator environment will also provide a standard platform for curriculum development in institutions of medical education.

Communication satellites

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Modern satellite communications systems (SATCOM) are often large and complex with many interacting parts and elements. In addition, the need for broadband connectivity on a moving vehicle has increased dramatically in the past few years for both commercial and military applications. To accurately predict and deliver high quality of service, SATCOM system designers have to factor in terrain as well as atmospheric and meteorological conditions in their planning. To deal with such complexity, system designers and operators increasingly turn towards computer models of their systems to simulate real-world operating conditions and gain insights into usability and requirements prior to final product sign-off. Modeling improves the understanding of the system by enabling the SATCOM system designer or planner to simulate real-world performance by injecting the models with multiple hypothetical atmospheric and environmental conditions. Simulation is often used in the training of civilian and military personnel. This usually occurs when it is prohibitively expensive or simply too dangerous to allow trainees to use the real equipment in the real world. In such situations, they will spend time learning valuable lessons in a "safe" virtual environment yet living a lifelike experience (or at least it is the goal). Often the convenience is to permit mistakes during training for a safety-critical system.

Digital lifecycle

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Simulation of airflow over an engine

Simulation solutions are being increasingly integrated with computer-aided solutions and processes (computer-aided design or CAD, computer-aided manufacturing or CAM, computer-aided engineering or CAE, etc.). The use of simulation throughout the product lifecycle, especially at the earlier concept and design stages, has the potential of providing substantial benefits. These benefits range from direct cost issues such as reduced prototyping and shorter time-to-market to better performing products and higher margins. However, for some companies, simulation has not provided the expected benefits.

The successful use of simulation, early in the lifecycle, has been largely driven by increased integration of simulation tools with the entire set of CAD, CAM and product-lifecycle management solutions. Simulation solutions can now function across the extended enterprise in a multi-CAD environment, and include solutions for managing simulation data and processes and ensuring that simulation results are made part of the product lifecycle history.

Disaster preparedness

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Simulation training has become a method for preparing people for disasters. Simulations can replicate emergency situations and track how learners respond thanks to a lifelike experience. Disaster preparedness simulations can involve training on how to handle terrorism attacks, natural disasters, pandemic outbreaks, or other life-threatening emergencies.

One organization that has used simulation training for disaster preparedness is CADE (Center for Advancement of Distance Education). CADE[76] has used a video game to prepare emergency workers for multiple types of attacks. As reported by News-Medical.Net, "The video game is the first in a series of simulations to address bioterrorism, pandemic flu, smallpox, and other disasters that emergency personnel must prepare for.[77]" Developed by a team from the University of Illinois at Chicago (UIC), the game allows learners to practice their emergency skills in a safe, controlled environment.

The Emergency Simulation Program (ESP) at the British Columbia Institute of Technology (BCIT), Vancouver, British Columbia, Canada is another example of an organization that uses simulation to train for emergency situations. ESP uses simulation to train on the following situations: forest fire fighting, oil or chemical spill response, earthquake response, law enforcement, municipal firefighting, hazardous material handling, military training, and response to terrorist attack[78] One feature of the simulation system is the implementation of "Dynamic Run-Time Clock," which allows simulations to run a 'simulated' time frame, "'speeding up' or 'slowing down' time as desired"[78] Additionally, the system allows session recordings, picture-icon based navigation, file storage of individual simulations, multimedia components, and launch external applications.

At the University of Québec in Chicoutimi, a research team at the outdoor research and expertise laboratory (Laboratoire d'Expertise et de Recherche en Plein Air – LERPA) specializes in using wilderness backcountry accident simulations to verify emergency response coordination.

Instructionally, the benefits of emergency training through simulations are that learner performance can be tracked through the system. This allows the developer to make adjustments as necessary or alert the educator on topics that may require additional attention. Other advantages are that the learner can be guided or trained on how to respond appropriately before continuing to the next emergency segment—this is an aspect that may not be available in the live environment. Some emergency training simulators also allow for immediate feedback, while other simulations may provide a summary and instruct the learner to engage in the learning topic again.

In a live-emergency situation, emergency responders do not have time to waste. Simulation-training in this environment provides an opportunity for learners to gather as much information as they can and practice their knowledge in a safe environment. They can make mistakes without risk of endangering lives and be given the opportunity to correct their errors to prepare for the real-life emergency.

Economics

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Simulations in economics and especially in macroeconomics, judge the desirability of the effects of proposed policy actions, such as fiscal policy changes or monetary policy changes. A mathematical model of the economy, having been fitted to historical economic data, is used as a proxy for the actual economy; proposed values of government spending, taxation, open market operations, etc. are used as inputs to the simulation of the model, and various variables of interest such as the inflation rate, the unemployment rate, the balance of trade deficit, the government budget deficit, etc. are the outputs of the simulation. The simulated values of these variables of interest are compared for different proposed policy inputs to determine which set of outcomes is most desirable.[79]

Engineering, technology, and processes

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Simulation is an important feature in engineering systems or any system that involves many processes. For example, in electrical engineering, delay lines may be used to simulate propagation delay and phase shift caused by an actual transmission line. Similarly, dummy loads may be used to simulate impedance without simulating propagation and is used in situations where propagation is unwanted. A simulator may imitate only a few of the operations and functions of the unit it simulates. Contrast with: emulate.[80]

Most engineering simulations entail mathematical modeling and computer-assisted investigation. There are many cases, however, where mathematical modeling is not reliable. Simulation of fluid dynamics problems often require both mathematical and physical simulations. In these cases the physical models require dynamic similitude. Physical and chemical simulations have also direct realistic uses, rather than research uses; in chemical engineering, for example, process simulations are used to give the process parameters immediately used for operating chemical plants, such as oil refineries. Simulators are also used for plant operator training. It is called Operator Training Simulator (OTS) and has been widely adopted by many industries from chemical to oil&gas and to the power industry. This created a safe and realistic virtual environment to train board operators and engineers. Mimic is capable of providing high fidelity dynamic models of nearly all chemical plants for operator training and control system testing.

Ergonomics

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Ergonomic simulation involves the analysis of virtual products or manual tasks within a virtual environment. In the engineering process, the aim of ergonomics is to develop and to improve the design of products and work environments.[81] Ergonomic simulation utilizes an anthropometric virtual representation of the human, commonly referenced as a mannequin or Digital Human Models (DHMs), to mimic the postures, mechanical loads, and performance of a human operator in a simulated environment such as an airplane, automobile, or manufacturing facility. DHMs are recognized as evolving and valuable tool for performing proactive ergonomics analysis and design.[82] The simulations employ 3D-graphics and physics-based models to animate the virtual humans. Ergonomics software uses inverse kinematics (IK) capability for posing the DHMs.[81]

Software tools typically calculate biomechanical properties including individual muscle forces, joint forces and moments. Most of these tools employ standard ergonomic evaluation methods such as the NIOSH lifting equation and Rapid Upper Limb Assessment (RULA). Some simulations also analyze physiological measures including metabolism, energy expenditure, and fatigue limits Cycle time studies, design and process validation, user comfort, reachability, and line of sight are other human-factors that may be examined in ergonomic simulation packages.[83]

Modeling and simulation of a task can be performed by manually manipulating the virtual human in the simulated environment. Some ergonomics simulation software permits interactive, real-time simulation and evaluation through actual human input via motion capture technologies. However, motion capture for ergonomics requires expensive equipment and the creation of props to represent the environment or product.

Some applications of ergonomic simulation in include analysis of solid waste collection, disaster management tasks, interactive gaming,[84] automotive assembly line,[85] virtual prototyping of rehabilitation aids,[86] and aerospace product design.[87] Ford engineers use ergonomics simulation software to perform virtual product design reviews. Using engineering data, the simulations assist evaluation of assembly ergonomics. The company uses Siemen's Jack and Jill ergonomics simulation software in improving worker safety and efficiency, without the need to build expensive prototypes.[88]

Finance

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In finance, computer simulations are often used for scenario planning. Risk-adjusted net present value, for example, is computed from well-defined but not always known (or fixed) inputs. By imitating the performance of the project under evaluation, simulation can provide a distribution of NPV over a range of discount rates and other variables. Simulations are also often used to test a financial theory or the ability of a financial model.[89]

Simulations are frequently used in financial training to engage participants in experiencing various historical as well as fictional situations. There are stock market simulations, portfolio simulations, risk management simulations or models and forex simulations. Such simulations are typically based on stochastic asset models. Using these simulations in a training program allows for the application of theory into a something akin to real life. As with other industries, the use of simulations can be technology or case-study driven.

Flight

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A military flight simulator

Flight simulation is mainly used to train pilots outside of the aircraft.[90] In comparison to training in flight, simulation-based training allows for practicing maneuvers or situations that may be impractical (or even dangerous) to perform in the aircraft while keeping the pilot and instructor in a relatively low-risk environment on the ground. For example, electrical system failures, instrument failures, hydraulic system failures, and even flight control failures can be simulated without risk to the crew or equipment.[91]

Instructors can also provide students with a higher concentration of training tasks in a given period of time than is usually possible in the aircraft. For example, conducting multiple instrument approaches in the actual aircraft may require significant time spent repositioning the aircraft, while in a simulation, as soon as one approach has been completed, the instructor can immediately reposition the simulated aircraft to a location from which the next approach can be begun.

Flight simulation also provides an economic advantage over training in an actual aircraft. Once fuel, maintenance, and insurance costs are taken into account, the operating costs of an FSTD are usually substantially lower than the operating costs of the simulated aircraft. For some large transport category airplanes, the operating costs may be several times lower for the FSTD than the actual aircraft. Another advantage is reduced environmental impact, as simulators don't contribute directly to carbon or noise emissions.[92]

There also exist "engineering flight simulators" which are a key element of the aircraft design process.[93] Many benefits that come from a lower number of test flights like cost and safety improvements are described above, but there are some unique advantages. Having a simulator available allows for faster design iteration cycle or using more test equipment than could be fit into a real aircraft.[94]

Marine

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A ship bridge simulator

Bearing resemblance to flight simulators, a marine simulator is meant for training of ship personnel. The most common marine simulators include:[95]

  • Ship's bridge simulators
  • Engine room simulators[96]
  • Cargo handling simulators
  • Communication / GMDSS simulators
  • ROV simulators

Simulators like these are mostly used within maritime colleges, training institutions, and navies. They often consist of a replication of a ships' bridge, with the operating console(s), and a number of screens on which the virtual surroundings are projected.

Military

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The grenade launcher trains using a computer simulator

Military simulations, also known informally as war games, are models in which theories of warfare can be tested and refined without the need for actual hostilities. They exist in many different forms, with varying degrees of realism. In recent times, their scope has widened to include not only military but also political and social factors (for example, the Nationlab series of strategic exercises in Latin America).[97] While many governments make use of simulation, both individually and collaboratively, little is known about the model's specifics outside professional circles.

Network and distributed systems

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Network and distributed systems have been extensively simulated in other to understand the impact of new protocols and algorithms before their deployment in the actual systems. The simulation can focus on different levels (physical layer, network layer, application layer), and evaluate different metrics (network bandwidth, resource consumption, service time, dropped packets, system availability). Examples of simulation scenarios of network and distributed systems are:

Payment and securities settlement system

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Simulation techniques have also been applied to payment and securities settlement systems. Among the main users are central banks who are generally responsible for the oversight of market infrastructure and entitled to contribute to the smooth functioning of the payment systems.

Central banks have been using payment system simulations to evaluate things such as the adequacy or sufficiency of liquidity available ( in the form of account balances and intraday credit limits) to participants (mainly banks) to allow efficient settlement of payments.[102][103] The need for liquidity is also dependent on the availability and the type of netting procedures in the systems, thus some of the studies have a focus on system comparisons.[104]

Another application is to evaluate risks related to events such as communication network breakdowns or the inability of participants to send payments (e.g. in case of possible bank failure).[105] This kind of analysis falls under the concepts of stress testing or scenario analysis.

A common way to conduct these simulations is to replicate the settlement logics of the real payment or securities settlement systems under analysis and then use real observed payment data. In case of system comparison or system development, naturally, also the other settlement logics need to be implemented.

To perform stress testing and scenario analysis, the observed data needs to be altered, e.g. some payments delayed or removed. To analyze the levels of liquidity, initial liquidity levels are varied. System comparisons (benchmarking) or evaluations of new netting algorithms or rules are performed by running simulations with a fixed set of data and varying only the system setups.

An inference is usually done by comparing the benchmark simulation results to the results of altered simulation setups by comparing indicators such as unsettled transactions or settlement delays.

Power systems

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Project management

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Project management simulation is simulation used for project management training and analysis. It is often used as a training simulation for project managers. In other cases, it is used for what-if analysis and for supporting decision-making in real projects. Frequently the simulation is conducted using software tools.

Robotics

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A robotics simulator is used to create embedded applications for a specific (or not) robot without being dependent on the 'real' robot. In some cases, these applications can be transferred to the real robot (or rebuilt) without modifications. Robotics simulators allow reproducing situations that cannot be 'created' in the real world because of cost, time, or the 'uniqueness' of a resource. A simulator also allows fast robot prototyping. Many robot simulators feature physics engines to simulate a robot's dynamics.

Production

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Simulation of production systems is used mainly to examine the effect of improvements or investments in a production system. Most often this is done using a static spreadsheet with process times and transportation times. For more sophisticated simulations Discrete Event Simulation (DES) is used with the advantages to simulate dynamics in the production system. A production system is very much dynamic depending on variations in manufacturing processes, assembly times, machine set-ups, breaks, breakdowns and small stoppages.[106] There is much software commonly used for discrete event simulation. They differ in usability and markets but do often share the same foundation.

Sales process

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Simulations are useful in modeling the flow of transactions through business processes, such as in the field of sales process engineering, to study and improve the flow of customer orders through various stages of completion (say, from an initial proposal for providing goods/services through order acceptance and installation). Such simulations can help predict the impact of how improvements in methods might impact variability, cost, labor time, and the number of transactions at various stages in the process. A full-featured computerized process simulator can be used to depict such models, as can simpler educational demonstrations using spreadsheet software, pennies being transferred between cups based on the roll of a die, or dipping into a tub of colored beads with a scoop.[107]

Sports

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In sports, computer simulations are often done to predict the outcome of events and the performance of individual sportspeople. They attempt to recreate the event through models built from statistics. The increase in technology has allowed anyone with knowledge of programming the ability to run simulations of their models. The simulations are built from a series of mathematical algorithms, or models, and can vary with accuracy. Accuscore, which is licensed by companies such as ESPN, is a well-known simulation program for all major sports. It offers a detailed analysis of games through simulated betting lines, projected point totals and overall probabilities.

With the increased interest in fantasy sports simulation models that predict individual player performance have gained popularity. Companies like What If Sports and StatFox specialize in not only using their simulations for predicting game results but how well individual players will do as well. Many people use models to determine whom to start in their fantasy leagues.

Another way simulations are helping the sports field is in the use of biomechanics. Models are derived and simulations are run from data received from sensors attached to athletes and video equipment. Sports biomechanics aided by simulation models answer questions regarding training techniques such as the effect of fatigue on throwing performance (height of throw) and biomechanical factors of the upper limbs (reactive strength index; hand contact time).[108]

Computer simulations allow their users to take models which before were too complex to run, and give them answers. Simulations have proven to be some of the best insights into both play performance and team predictability.

Space shuttle countdown

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Firing Room 1 configured for Space Shuttle launches

Simulation was used at Kennedy Space Center (KSC) to train and certify Space Shuttle engineers during simulated launch countdown operations. The Space Shuttle engineering community would participate in a launch countdown integrated simulation before each Shuttle flight. This simulation is a virtual simulation where real people interact with simulated Space Shuttle vehicle and Ground Support Equipment (GSE) hardware. The Shuttle Final Countdown Phase Simulation, also known as S0044, involved countdown processes that would integrate many of the Space Shuttle vehicle and GSE systems. Some of the Shuttle systems integrated in the simulation are the main propulsion system, RS-25, solid rocket boosters, ground liquid hydrogen and liquid oxygen, external tank, flight controls, navigation, and avionics.[109] The high-level objectives of the Shuttle Final Countdown Phase Simulation are:

  • To demonstrate firing room final countdown phase operations.
  • To provide training for system engineers in recognizing, reporting and evaluating system problems in a time critical environment.
  • To exercise the launch team's ability to evaluate, prioritize and respond to problems in an integrated manner within a time critical environment.
  • To provide procedures to be used in performing failure/recovery testing of the operations performed in the final countdown phase.[110]

The Shuttle Final Countdown Phase Simulation took place at the Kennedy Space Center Launch Control Center firing rooms. The firing room used during the simulation is the same control room where real launch countdown operations are executed. As a result, equipment used for real launch countdown operations is engaged. Command and control computers, application software, engineering plotting and trending tools, launch countdown procedure documents, launch commit criteria documents, hardware requirement documents, and any other items used by the engineering launch countdown teams during real launch countdown operations are used during the simulation.

The Space Shuttle vehicle hardware and related GSE hardware is simulated by mathematical models (written in Shuttle Ground Operations Simulator (SGOS) modeling language[111]) that behave and react like real hardware. During the Shuttle Final Countdown Phase Simulation, engineers command and control hardware via real application software executing in the control consoles – just as if they were commanding real vehicle hardware. However, these real software applications do not interface with real Shuttle hardware during simulations. Instead, the applications interface with mathematical model representations of the vehicle and GSE hardware. Consequently, the simulations bypass sensitive and even dangerous mechanisms while providing engineering measurements detailing how the hardware would have reacted. Since these math models interact with the command and control application software, models and simulations are also used to debug and verify the functionality of application software.[112]

Satellite navigation

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The only true way to test GNSS receivers (commonly known as Sat-Nav's in the commercial world) is by using an RF Constellation Simulator. A receiver that may, for example, be used on an aircraft, can be tested under dynamic conditions without the need to take it on a real flight. The test conditions can be repeated exactly, and there is full control over all the test parameters. this is not possible in the 'real-world' using the actual signals. For testing receivers that will use the new Galileo (satellite navigation) there is no alternative, as the real signals do not yet exist.

Trains

[edit]

Weather

[edit]

Predicting weather conditions by extrapolating/interpolating previous data is one of the real use of simulation. Most of the weather forecasts use this information published by Weather bureaus. This kind of simulations helps in predicting and forewarning about extreme weather conditions like the path of an active hurricane/cyclone. Numerical weather prediction for forecasting involves complicated numeric computer models to predict weather accurately by taking many parameters into account.

Simulation games

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Strategy games—both traditional and modern—may be viewed as simulations of abstracted decision-making for the purpose of training military and political leaders (see History of Go for an example of such a tradition, or Kriegsspiel for a more recent example).

Many other video games are simulators of some kind. Such games can simulate various aspects of reality, from business, to government, to construction, to piloting vehicles (see above).

Historical usage

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Historically, the word had negative connotations:

...therefore a general custom of simulation (which is this last degree) is a vice, using either of a natural falseness or fearfulness...

— Francis Bacon, Of Simulation and Dissimulation, 1597

...for Distinction Sake, a Deceiving by Words, is commonly called a Lye, and a Deceiving by Actions, Gestures, or Behavior, is called Simulation...

— Robert South, South, 1697, p.525

However, the connection between simulation and dissembling later faded out and is now only of linguistic interest.[113]

See also

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Fields of study

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Specific examples & literature

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A simulation is an imitative representation of the operation or features of a real-world process, system, or phenomenon, often employing mathematical models run on computers to approximate behaviors, test hypotheses, or evaluate outcomes under controlled conditions. Computer simulations, in particular, use step-by-step algorithms to explore the dynamics of complex models that would be impractical or impossible to study empirically. Emerging from wartime computational techniques like the in the 1940s, simulation has become indispensable in fields such as for virtual prototyping, for mission planning, for procedural training, and physics for modeling phenomena from particle interactions to climate systems. While simulations provide predictive power grounded in causal mechanisms and validated against empirical data, their approximations can introduce uncertainties requiring careful verification. A notable philosophical extension is the , argued by in 2003, which contends that if posthuman civilizations can run vast numbers of simulations, it is statistically likely that we inhabit one rather than base reality.

Definition and Classification

Core Concepts and Terminology

A simulation is the of executing or experimenting with a model to achieve objectives such as , , or . It involves imitating the of a real or proposed over time to predict outcomes or test scenarios without direct experimentation on the actual . Central to simulation is the model, defined as an or representation of a , , or , often constructed using logical rules, mathematical equations, or differential equations to capture essential features while omitting irrelevant details. A simulation model specifically employs logic and equations to represent dynamic interactions abstractly, enabling repeatable experimentation under controlled conditions. Simulations are classified along several dimensions, beginning with static versus dynamic. Static simulations represent systems at a fixed point without time progression, such as methods for estimating probabilities in non-temporal scenarios. Dynamic simulations, by contrast, incorporate time as a variable, modeling how systems evolve due to internal dynamics or external inputs, as in queueing or inventory systems. Within dynamic simulations, continuous simulations use models based on differential equations where state variables change smoothly over continuous time, suitable for physical processes like . Discrete-event simulations, conversely, advance time in discrete jumps triggered by events, with state changes occurring only at specific instants, common in or modeling. Another key distinction is deterministic versus . Deterministic simulations produce identical outputs for the same inputs, assuming no and fully predictable behavior, as in fixed-rate production lines. Stochastic simulations incorporate random variables drawn from probability distributions to account for uncertainty, requiring multiple runs to estimate statistical properties like means or variances, exemplified by service time variability in queues. Ensuring reliability involves verification, which checks that the model's implementation accurately reflects its and specifications—"building the thing right." Validation assesses whether the model faithfully represents the real-world system's behavior for its intended purpose—"building the right thing"—often through comparison with empirical data. follows, providing formal endorsement by an authority that the simulation is suitable for specific applications, such as or .

Types of Simulations

Simulations are broadly classified by their temporal structure, determinism, and modeling paradigm, which determine how they represent system dynamics and uncertainty. Discrete simulations model state changes occurring at specific, irregular points in time, often triggered by events such as arrivals or failures, making them suitable for systems like manufacturing lines or queueing networks where continuous monitoring is inefficient. Continuous simulations, by contrast, approximate smooth variations over time using differential equations, ideal for physical processes like chemical reactions or fluid flows where variables evolve incrementally. These distinctions arise from the underlying mathematics: discrete-event approaches advance time to the next event, minimizing computational steps, while continuous methods integrate equations across fixed or variable time steps. Deterministic simulations yield identical outputs for given inputs, relying on fixed rules without , as in planetary calculations solved via Newton's laws. simulations introduce probabilistic elements, such as random variables drawn from distributions, to capture real-world variability, enabling in fields like or ; for instance, methods perform repeated random sampling to estimate outcomes like pi's value or portfolio volatility, with accuracy improving as the number of trials increases—typically converging at a rate of 1/sqrt(N) where N is the sample size. Beyond these axes, specialized paradigms address complex interactions. Agent-based simulations model autonomous entities (agents) following simple rules, whose collective behaviors yield emergent phenomena, as in ecological models of predator-prey dynamics or economic markets where individual decisions drive macro trends without centralized control. simulations employ stocks, flows, and feedback loops to depict aggregated system evolution, originating from Forrester's work in the for industrial applications and later adapted for , such as urban growth projections. Hybrid approaches combine elements, like discrete events within continuous frameworks, to handle multifaceted systems such as power grids integrating sudden faults with ongoing load variations. These categories are not mutually exclusive but guide based on system characteristics, with validation against empirical data essential to ensure fidelity.

Historical Development

Early Analog and Physical Simulations

Physical simulations predated analog computational devices, relying on scaled physical models to replicate real-world phenomena under controlled conditions. In , reduced-scale models emerged in the to study free-surface flows, such as dynamics and wave interactions, allowing engineers to predict behaviors like scour or without full-scale risks. These models adhered to principles of , ensuring geometric, kinematic, and dynamic similarities to prototypes, as formalized by researchers like William Froude for ship hydrodynamics in the . In , physical models tested bridge and designs from the late , using materials like plastics or to simulate stress distributions and modes under load. Analog simulations employed mechanical or electromechanical systems to mimic continuous processes, solving differential equations through proportional physical representations. Tide-predicting machines, developed by William Thomson () in the 1870s, were early examples: these harmonic analyzers used rotating gears and cams to sum sinusoidal components of tidal forces, generating predictions for specific ports by mechanically integrating astronomical data. By the early , such devices processed up to 40 tidal constituents with accuracies rivaling manual calculations, aiding and coastal planning until digital alternatives supplanted them. The differential analyzer, constructed by and Harold Hazen at MIT between 1928 and 1931, marked a advancement in general-purpose analog simulation. This mechanical device integrated differential equations via interconnected shafts, integrators, and servo-motors, simulating systems like networks and ballistic trajectories with outputs plotted continuously. It comprised six integrators and handled nonlinear problems through function-specific cams, reducing computation times from weeks to hours for complex engineering analyses. In , Edwin Link's Trainer, patented in , provided the first electromechanical flight simulation for instrument training. The device used pneumatic bellows, , and a to replicate aircraft motion and attitude, with a tilting on a to induce realistic disorientation cues. Deployed widely by 1934, it trained pilots on without flight risks, proving essential for military adoption pre-World War II. These early analogs demonstrated causal mappings from physical laws—such as torque for rotation or fluid displacement for integration—to model dynamic systems, laying groundwork for later hybrid and digital methods despite limitations in precision and scalability.

Emergence of Computer-Based Simulation

The development of computer-based simulation emerged primarily during and immediately after , driven by the need to model complex probabilistic processes that defied analytical solutions, such as neutron diffusion in . In 1946, mathematician Stanislaw Ulam, while recovering from illness and reflecting on solitaire probabilities, conceived the , a statistical sampling technique inspired by casino games to approximate solutions through random trials. quickly recognized its applicability to Los Alamos National Laboratory's challenges in simulating atomic bomb implosion dynamics, where physical experiments were prohibitively dangerous and expensive. This method marked the shift from deterministic analog devices to probabilistic digital computation, leveraging emerging electronic computers to handle vast ensembles of random paths. The Electronic Numerical Integrator and Computer (), completed in December 1945 as the first programmable general-purpose electronic digital computer, facilitated the first automated simulations. Initially designed for U.S. Army ballistic trajectory calculations, ENIAC was reprogrammed post-war for nuclear simulations; in 1947, von Neumann proposed adapting it for Los Alamos problems, leading to the first runs in April 1948 by a team including von Neumann, , and others. These simulations modeled neutron behavior in fission weapons by generating thousands of random particle paths, yielding results that informed weapon design despite ENIAC's limitations—such as 18,000 vacuum tubes, manual rewiring for programs, and computation times spanning days for modest ensembles. The effort required shipping ENIAC to and training personnel, underscoring the era's computational constraints, yet it demonstrated digital computers' superiority over analog predecessors for stochastic modeling. By the early 1950s, as stored-program computers like (conceptualized by von Neumann in 1945) and (delivered 1951) proliferated, simulation extended beyond to and . These machines enabled discrete-event simulations for and , though high costs and long run times—often requiring custom coding in machine language—limited accessibility to government and military-funded projects. The availability of general-purpose electronic computers catalyzed a proliferation of simulation techniques, laying groundwork for domain-specific languages like SIMSCRIPT in the , but early adoption was hampered by the need for expert programmers and validation against sparse empirical data. This period established simulation as a causal tool for exploring "what-if" scenarios in irreducible systems, prioritizing empirical benchmarking over idealized models.

Post-2000 Milestones and Expansion

The advent of general-purpose on processing units (GPUs) marked a pivotal advancement in simulation capabilities, with NVIDIA's release of the platform in November 2006 enabling parallel for compute-intensive tasks such as (CFD) and , achieving speedups of orders of magnitude over CPU-only methods in suitable applications. This hardware innovation, building on earlier GPU experiments, democratized high-performance simulations by leveraging the massive parallelism inherent in GPU architectures, reducing computation times for large-scale models from days to hours. The formalization of digital twins—dynamic virtual representations of physical assets that integrate for predictive simulation—occurred in 2002 when Michael Grieves introduced the concept in a presentation on product lifecycle management, emphasizing mirrored data flows between physical and virtual entities. NASA's adoption and popularization of the term in 2010 further propelled its integration into and manufacturing, where digital twins enabled continuous monitoring and scenario testing without physical prototypes, reducing development costs and time. By the mid-2010s, companies like GE implemented digital twins for in industrial turbines, correlating sensor data with simulation models to forecast failures with high fidelity. Cloud-based simulation platforms emerged alongside the commercialization of cloud infrastructure, with (AWS) launching its Elastic Compute Cloud (EC2) in 2006, providing scalable resources that alleviated hardware barriers for running resource-heavy simulations. This shift facilitated the third generation of simulation tools—cloud-native environments like , introduced around 2012—which offered collaborative, browser-accessible multiphysics modeling without local installations, expanding access to small firms and researchers. Hardware performance, as quantified by SPEC benchmarks, improved over two orders of magnitude from 2000 onward, compounded by multi-core CPUs and cloud elasticity, enabling simulations of unprecedented scale, such as billion-atom molecular systems or global climate models. Post-2000 expansion reflected broader industrial adoption, driven by Industry 4.0 paradigms, where simulations transitioned from siloed analysis to integrated digital threads in , testing, and operations. The global market, valued at approximately $5-10 billion in the early , surged due to these enablers, reaching projections of $36.22 billion by 2030, with dominant players like and advancing GPU-accelerated solvers and AI-hybrid models for applications in automotive crash testing and aerodynamic optimization. In pharmaceuticals, simulations proliferated, with projects like scaling to petascale operations by the late , simulating protein-ligand interactions at timescales of microseconds, informing pipelines.30684-6) Engineering fields saw simulation-driven virtual prototyping reduce physical iterations by up to 50% in sectors like , exemplified by NASA's use of high-fidelity CFD for next-generation . This era's milestones underscored simulation's causal role in and empirical validation, prioritizing verifiable model fidelity over idealized assumptions amid growing computational realism.

Technical Foundations of Simulation

Analog and Hybrid Methods

Analog simulation methods employ continuous physical phenomena, such as electrical voltages or mechanical displacements, to model the behavior of dynamic systems, particularly those governed by differential equations. These systems represent variables through proportional physical quantities, enabling real-time computation via components like s configured for integration, differentiation, and . For instance, an integrator circuit using an solves equations of the form dxdt=f(x,t)\frac{dx}{dt} = f(x, t) by accumulating input signals over time, with voltage levels directly analogous to state variables. This approach excels in simulating continuous processes, such as feedback control systems or , due to inherent parallelism and continuous-time operation, which avoids errors inherent in digital methods. Early electronic analog computers, developed in the mid-20th century, were widely applied in for solving ordinary differential equations (ODEs) modeling phenomena like ballistic trajectories and chemical reactions. A typical setup might use 20-100 amplifiers patched via a switchboard to form computational graphs, achieving simulation speeds scaled to real-time by adjusting time constants with potentiometers. Precision was limited to about 0.1% due to component tolerances and drift, but this sufficed for many pre-1960s applications where qualitative behavior and rapid iteration outweighed exact numerical accuracy. In academia and industry, such systems simulated seismic instruments and servomechanisms, providing intuitive visualization through oscilloscope traces of variable trajectories. Hybrid methods integrate analog circuitry for continuous subsystems with digital components for discrete logic, lookup tables, or high-precision arithmetic, addressing limitations of pure analog setups like scalability and storage. Developed prominently in the , hybrid computers interfaced via analog-to-digital and digital-to-analog converters, allowing digital oversight of analog patches for tasks such as iterative optimization or event handling in simulations. For example, NASA's hybrid systems combined analog real-time dynamics with digital sequencing to model control laws, reducing setup time through automated scaling and improving accuracy to 10^{-4} in hybrid loops. This architecture was particularly effective for stiff differential equations, where analog components handled fast transients while digital elements managed slow variables or nonlinear functions via piecewise approximations. Despite the dominance of digital simulation since the , analog and hybrid techniques persist in niche areas like high-speed and real-time embedded systems, where low-latency continuous modeling outperforms sampled digital equivalents. Modern implementations, often using field-programmable analog arrays, simulate integro-differential equations with conductances tuned for specific kernels, demonstrating utility in resource-constrained environments. However, challenges including sensitivity to and component aging necessitate , limiting widespread adoption outside specialized hardware-in-the-loop testing.

Digital Simulation Architectures

Digital simulation architectures encompass the software and hardware frameworks designed to execute computational models mimicking real-world systems, emphasizing in handling complex dynamics through structured flow, processing, and mechanisms. These architectures integrate modeling paradigms with computational resources, ranging from single-threaded sequential execution on general-purpose CPUs to systems exploiting GPUs and distributed clusters for . Core elements include model abstraction layers for defining system states and transitions, solver engines for or event processing, and interfaces for handling, often implemented in languages like C++, Python, or domain-specific ones such as . Event-driven architectures dominate discrete simulations, where system evolution is propelled by timestamped events rather than fixed time steps, enabling efficient handling of sparse activity in systems like queueing networks or network protocols; for instance, compilers in tools like NS-3 process event queues to simulate packet-level behaviors with sub-millisecond resolution in large topologies. In contrast, time-stepped architectures suit continuous simulations, discretizing time into uniform increments for solving differential equations, as seen in methods for partial differential equations in , where stability requires adaptive step-sizing to prevent numerical . Parallel and distributed architectures address scalability limits of sequential systems by partitioning models across cores or nodes; synchronous parallel simulation, as in time-warp protocols, advances logical processes in to maintain , while optimistic approaches erroneous computations using state-saving checkpoints, achieving up to 10x speedups in large-scale or models on clusters with thousands of processors. Hardware accelerations, such as GPU-based architectures utilizing for matrix-heavy operations, enable real-time simulation of millions of particles in , with peak throughputs exceeding 100 TFLOPS on systems like A100 GPUs deployed since 2020. Field-programmable gate arrays (FPGAs) offer reconfigurable logic for cycle-accurate , reducing simulation latency by orders of magnitude compared to software interpreters in validating processor designs. Hybrid architectures combine discrete and continuous elements, employing co-simulation frameworks to interface event-based and solvers, critical for cyber-physical systems like automotive controls where interacts with physical plant models; standards like (FMI), adopted since 2010, facilitate modular interoperability across tools from vendors like and . These architectures prioritize and , with verification techniques including statistical validation against empirical data to mitigate errors from approximations, as non-deterministic parallelism can introduce variability exceeding 5% in convergence metrics without proper synchronization.

Key Algorithms and Modeling Techniques

Simulation modeling techniques encompass a range of approaches to represent real-world systems computationally. Deterministic models compute outputs solely from inputs without probabilistic elements, yielding reproducible results ideal for systems with known causal mechanisms, such as planetary motion simulations. models, conversely, integrate randomness via probability distributions to capture uncertainty, enabling analysis of variability in outcomes like assessments. Models are further categorized as static, which evaluate systems at a single point without temporal evolution, or dynamic, which track changes over time; and discrete, operating on event-driven or stepwise updates, versus continuous, which model smooth variations through differential equations. Key algorithms underpin these techniques, particularly for solving governing equations. The discretizes spatial and temporal domains into grids, approximating derivatives with difference quotients to solve partial differential equations numerically; for instance, it underpins the finite-difference time-domain (FDTD) approach for electromagnetic wave propagation, where the Yee algorithm staggers electric and components on a grid to ensure stability up to the Courant limit. This method's second-order accuracy in space and time facilitates simulations of wave phenomena but requires fine grids for precision, increasing computational cost. Monte Carlo algorithms address modeling by generating numerous random samples from input probability distributions to approximate expected values or distributions of complex functions, as formalized in the 1940s for problems at Los Alamos. The process involves defining random variables, sampling via pseudorandom number generators, and aggregating results—often millions of iterations—to estimate integrals or probabilities, with variance reduction techniques like enhancing efficiency for high-dimensional problems in physics and finance. Agent-based modeling techniques simulate decentralized systems by defining autonomous agents with local rules, attributes, and interaction protocols, allowing emergent macroscopic behaviors to arise from micro-level decisions without central coordination. Implemented via iterative updates where agents perceive environments, act, and adapt—often using cellular automata or graph-based —this approach excels in capturing heterogeneity and non-linear dynamics, as seen in epidemiological models tracking individual contacts and behaviors. Validation relies on against empirical data, though computational demands scale with agent count. For continuous dynamic systems, numerical integration algorithms like the explicit provide first-order approximations by stepping forward in time via yn+1=yn+hf(tn,yn)y_{n+1} = y_n + h f(t_n, y_n), where hh is the time step, suitable for stiff-free ODEs but prone to without small steps. Higher-order variants, such as Runge-Kutta methods (e.g., fourth-order RK4), achieve greater accuracy by evaluating the derivative multiple times per step, balancing precision and cost in simulations of mechanical or . algorithms, meanwhile, maintain an event queue ordered by timestamps, advancing simulation time only to the next event to process state changes, optimizing efficiency for queueing systems like manufacturing lines.

Applications in Physical Sciences and Engineering

Physics and Mechanics Simulations

Physics and simulations employ numerical techniques to approximate solutions to equations governing motion, forces, and interactions in physical systems, enabling predictions of behaviors intractable analytically. Core methods include schemes for discretizing partial differential equations (PDEs) like the Navier-Stokes equations in , Monte Carlo integration for stochastic processes such as particle , and direct integration of ordinary differential equations (ODEs) for dynamical systems. These approaches leverage computational power to model phenomena from atomic scales to macroscopic structures, often validated against experimental data for accuracy. In mechanics, the (FEM) dominates for continuum problems, dividing domains into finite elements to solve variational formulations of elasticity, , and vibration. Developed mathematically in the and implemented digitally by the , FEM approximates field variables via basis functions, minimizing errors through mesh refinement. Applications span for seismic analysis of buildings, where simulations optimize designs against dynamic loads, and for predicting wing stresses under aerodynamic forces. For instance, FEM models verify structural integrity in high-pressure pipelines, forecasting failure points under thermal and mechanical stresses to prevent catastrophic leaks. Molecular dynamics (MD) simulations extend mechanics to atomic resolutions, evolving ensembles of particles under empirical potentials like Lennard-Jones for van der Waals forces, integrated via Verlet algorithms over femtosecond timesteps. These track trajectories to compute properties such as tensile strength in nanomaterials or fracture propagation in composites, bridging microscopic interactions to macroscopic failure modes. In engineering, MD informs alloy design by simulating defect diffusion, with results upscaled via hybrid MD-FEM frameworks for multiscale analysis of deformation kinetics. Validation relies on matching simulated pair correlation functions to scattering experiments, ensuring causal fidelity to interatomic forces. Coupled simulations integrate these techniques for complex systems, such as embedding regions within FEM meshes to capture localized plasticity amid global deformations, as in assessments of vehicle chassis. Physics-based simulations accelerate cycles by reducing physical prototypes; Aberdeen Group studies indicate firms using them early achieve 20-50% cost reductions in design iterations. Advances in enable billion-atom runs, enhancing predictions for extreme conditions like hypersonic flows or materials.

Chemical and Material Processes

Simulations of chemical processes employ computational methods to model reaction kinetics, , and , enabling prediction of outcomes in reactors, columns, and other unit operations without extensive physical experimentation. These models often integrate differential equations derived from and balances, solved numerically via software like Aspen Plus or gPROMS, which have been standard in since the 1980s for and optimization. For instance, facilitates , where variables such as temperature or catalyst loading are varied to assess impacts on yield, with accuracy validated against data showing deviations typically under 5-10% for well-characterized systems. In , () simulations track atomic trajectories under Newtonian mechanics, revealing microstructural evolution, diffusion coefficients, and mechanical properties at timescales. Classical , using force fields like Embedded Atom Method for metals, has predicted migration rates in alloys with errors below 20% compared to experiments, as demonstrated in studies of nanocrystalline . Quantum mechanical approaches, particularly (), compute ground-state electron densities to forecast band gaps, adsorption energies, and catalytic activity; for example, screenings of oxides for reactions identified candidates with overpotentials reduced by 0.2-0.5 V relative to standard benchmarks. These methods scale with computational power, with exascale simulations in 2022 enabling billion-atom systems for polymer composites. Hybrid techniques combine DFT with MD for , such as reactive force fields (ReaxFF) that simulate bond breaking in or processes, accurately reproducing activation energies within 10 kcal/mol of values. In battery materials, simulations have guided lithium-ion design by predicting shells and ion conductivities, contributing to electrolytes with 20-30% higher stability windows. methods complement these by sampling phase equilibria, as in predicting polymer crystallinity via configurational biases, where agreement with scattering data reaches 95% for melts. Despite advances, limitations persist in capturing or long-time scales, often addressed via enhanced sampling like , though validation against empirical data remains essential due to force field approximations. Recent integrations of accelerate these simulations; for instance, potentials trained on DFT data reduce computation times by orders of magnitude while maintaining chemical accuracy for molecular crystals, as shown in 2025 models for . In , stochastic simulations via Gillespie's algorithm model noisy reaction networks in microreactors, predicting product distributions for oscillatory systems like Belousov-Zhabotinsky with to time-series data. These tools underpin sustainable , such as CO2 capture sorbents optimized via grand canonical , yielding capacities 15-25% above experimental baselines for metal-organic frameworks. Overall, such simulations reduce development cycles from years to months, though institutional biases in academic reporting may overstate predictive successes without rigorous cross-validation.

Automotive and Aerospace Engineering

In , simulations enable virtual prototyping and testing of vehicle designs prior to physical construction, reducing development costs and time. Engineers employ finite element analysis (FEA) to model structural integrity during crash scenarios, simulating deformations and energy absorption in vehicle frames and components. For instance, the (NHTSA) utilizes full-vehicle finite element models (FEM) that incorporate interior details and occupant restraint systems to predict crash outcomes for driver and front-passenger positions. These models, often implemented in software like or , allow iterative design refinements to enhance without conducting numerous physical tests. Driving simulators further support automotive applications by facilitating driver training and human factors research. Mechanical Simulation Corporation, founded in 1996, commercialized university-derived technology for simulation, enabling realistic modeling of handling, braking, and stability. Early innovations trace to the , where the first automated driving simulations were developed to study forward collision warnings and systems. Such tools replicate real-world conditions, including adverse weather and traffic, to train drivers and validate advanced driver-assistance systems (ADAS), with studies confirming improvements in novice driver skills and safety awareness. In , (CFD) dominates for analyzing airflow over aircraft surfaces, optimizing lift, drag, and fuel efficiency. NASA's CFD efforts, outlined in the 2014 CFD Vision 2030 study, aim for revolutionary simulations of entire aircraft across flight envelopes, including transient engine behaviors and multi-disciplinary interactions. Historical advancements include the adoption of in the , which enabled complex fluid flow predictions previously limited by computational power. These simulations, validated against data, have informed designs like the F/A-18 by integrating CFD with tools such as . Aerospace simulations extend to and load computations, where CFD methods extract dynamic responses for and design validation. By 2023, milestones supported high-fidelity CFD for unconventional configurations, reducing reliance on costly prototypes while ensuring structural safety under extreme conditions. Across both fields, hybrid approaches combining FEA, CFD, and multi-body dynamics yield predictive accuracy, though real-world validation remains essential to account for material variabilities and unmodeled phenomena.

Applications in Life Sciences and Healthcare

Biological and Biomechanical Models

Biological simulations model dynamic processes in living organisms, such as cellular signaling, metabolic pathways, and , using mathematical equations to predict outcomes under varying conditions. These models often employ ordinary differential equations (ODEs) to represent biochemical reaction networks, as seen in approaches that integrate data for hypothesis testing and mechanism elucidation. For instance, the Hodgkin-Huxley model, developed in 1952, simulates neuronal action potentials via voltage-gated ion channels, providing a foundational framework validated against empirical data. Recent advances incorporate multi-scale modeling, combining molecular-level details with tissue-scale behaviors, enabled by computational power increases that allow simulation of complex interactions like gene regulatory networks. Agent-based models simulate individual entities, such as cells or organisms, interacting in environments to emerge population-level phenomena, useful for studying evolutionary dynamics or spread without assuming mean-field approximations. Spatial models extend this by incorporating and , employing partial differential equations (PDEs) or lattice-based methods to capture reaction- systems in tissues, as in tumor growth simulations where nutrient gradients drive cell proliferation patterns. Mechanistic models bridge wet-lab data with predictions, for example, in cell cycle regulation, where hybrid ODE-stochastic simulations replicate checkpoint controls and cyclin oscillations, aiding drug target identification by forecasting perturbation effects. Validation relies on parameter fitting to experimental datasets, though challenges persist in handling parameter uncertainty and non-identifiability, addressed via in modern frameworks. Biomechanical models simulate the mechanical behavior of biological structures, integrating anatomy, material properties, and external loads to analyze forces, deformations, and . Finite element analysis (FEA) discretizes tissues into meshes to solve PDEs for stress-strain responses, applied in where —positing adaptation to mechanical stimuli—is computationally tested against micro-CT scans showing trabecular alignment under load. Musculoskeletal simulations, such as those using OpenSim software, optimize muscle activations to reproduce observed via , computing joint torques from data with errors below 5% for cycles in healthy subjects. These models incorporate Hill-type muscle contracts, calibrated to force-velocity relationships from experiments, enabling predictions of injury risk in scenarios like ACL tears during pivoting maneuvers. Multibody dynamics couple rigid segments with soft tissues, simulating whole-body movements under and contact forces, as in forward simulations predicting metabolic costs from electromyography-validated activations. Recent integrations of AI accelerate surrogate modeling, reducing FEA computation times from hours to seconds for patient-specific organ simulations, enhancing applications in surgical planning where preoperative models predict post-operative with 10-15% accuracy improvements over traditional methods. Fluid-structure interactions model cardiovascular flows, using (CFD) to quantify wall shear stresses in arteries, correlated with formation risks from clinical imaging cohorts. Limitations include assumptions of linear elasticity in nonlinear tissues and validation gaps , mitigated by hybrid experimental-computational pipelines incorporating or MRI-derived properties.

Clinical Training and Patient Safety

Simulation-based training (SBT) employs high-fidelity mannequins, systems, and standardized patient actors to replicate clinical environments, enabling healthcare professionals to procedures, , and interdisciplinary coordination without exposing actual patients to harm. This method addresses gaps in traditional models, where real-time errors can lead to adverse outcomes, by providing deliberate in controlled settings. Studies indicate that SBT fosters proficiency in technical skills such as , central line insertion, and surgical techniques, with learners demonstrating higher competence post-training compared to lecture-based alternatives. Empirical evidence supports SBT's role in reducing medical errors and enhancing . A 2021 systematic review and of proficiency-based progression (PBP) training, a structured SBT variant, reported a standardized mean difference of -2.93 in error rates (95% CI: -3.80 to -2.06; P < 0.001) versus conventional methods, attributing improvements to iterative feedback and mastery thresholds. Similarly, simulations for clinical skills have yielded 40% fewer errors in subsequent practical assessments, as procedural repetition reinforces causal pathways between actions and outcomes. Targeted SBT for administration and response has also lowered rates; for instance, trainees using low-fidelity simulators showed sustained gains in error avoidance during high-stakes scenarios. In applications, SBT excels at latent system failures, such as communication breakdowns or equipment misuse, which contribute to up to 80% of sentinel events per root-cause analyses. Programs simulating rare occurrences—like obstetric hemorrhages or cardiac arrests—have improved team performance metrics, including time to intervention and adherence to protocols, correlating with real-world reductions in morbidity. The Agency for Healthcare Research and Quality (AHRQ) has funded over 160 simulation initiatives since 2000, documenting decreased preventable harm through process testing and human factors training, though long-term transfer to clinical settings requires institutional integration beyond isolated sessions. Despite these benefits, varies by simulator and trainee experience, with lower-resource settings relying on hybrid models to achieve comparable safety gains.

Epidemiological and Drug Development Simulations

Epidemiological simulations model the dynamics of infectious disease spread within populations, employing compartmental models such as the Susceptible-Infectious-Recovered () framework or its extensions like SEIR, which divide populations into states based on disease status and transition rates derived from empirical data on transmission, recovery, and mortality. These deterministic models, originating from Kermack and McKendrick's 1927 work, enable forecasting of outbreak trajectories and evaluation of interventions like or lockdowns by simulating parameter variations, though their accuracy depends on precise inputs for reproduction numbers (R0) and contact rates, which can vary regionally and temporally. Agent-based models, such as those implemented in software like Epiabm or Pyfectious, offer alternatives by representing individuals with attributes like age, mobility, and behavior, allowing simulation of heterogeneous networks and superspreading events, as demonstrated in reconstructions of scenarios where individual-level propagation revealed optimal strategies. Despite their utility in policy scenarios, epidemiological models face inherent limitations in predictive accuracy due to uncertainties in , such as underreporting of cases or delays in , which introduce biases amplifying errors in short-term forecasts. Computational intractability arises in network-based predictions, where even approximating properties like peak timing proves NP-hard for certain graphs, constraining scalability for real-time applications without simplifications that risk oversimplification of causal pathways like behavioral adaptations. Recent integrations of with mechanistic models, reviewed in 2025 scoping analyses, aim to mitigate these by learning from historical outbreaks across diseases like and , yet validation remains challenged by to biased datasets from academic sources prone to selective reporting. In , computational simulations accelerate candidate identification through methods like molecular docking and dynamics, which predict ligand-protein binding affinities by solving equations for intermolecular forces and conformational changes, reducing reliance on costly wet-lab screening. For instance, multiscale biomolecular simulations elucidate drug-target interactions at atomic resolution, as in virtual screening of billions of compounds against , identifying leads that advanced to trials faster than traditional high-throughput methods. trajectories, running on GPU-accelerated platforms, forecast like absorption, distribution, metabolism, and excretion () properties, with 2023 reviews highlighting their role in optimizing by simulating and contributions often missed in static models. AI-enhanced simulations have notably improved early-stage success rates, with Phase I trials for computationally discovered molecules achieving 80-90% progression in recent cohorts, surpassing historical industry averages of 40-65%, attributed to generative models prioritizing viable chemical spaces. However, overall pipeline attrition remains high at around 85% from discovery to approval, as predictions falter on complex factors like off-target effects or immune responses not fully captured without hybrid experimental validation. Regulatory acceptance, as per FDA's guidance on model-informed development, hinges on rigorous qualification, yet biases in training data from underdiverse clinical cohorts can propagate errors, underscoring the need for causal validation over correlative fits.

Applications in Social Sciences and Economics

Economic Modeling and Forecasting

Economic simulations in modeling and forecasting replicate complex interactions within economies using computational techniques to predict aggregate behaviors, test policy interventions, and assess risks under various scenarios. These models often incorporate stochastic processes to account for uncertainty, such as Monte Carlo methods that generate probability distributions of outcomes by running thousands of iterations with randomized inputs drawn from empirical data. Dynamic Stochastic General Equilibrium (DSGE) models, a cornerstone of modern central bank forecasting, solve for equilibrium paths of economic variables like output and inflation in response to shocks, assuming rational agents and market clearing; for instance, the New York Federal Reserve's DSGE model produces quarterly forecasts of key macro variables including GDP growth and unemployment. Agent-based models (ABMs) represent an alternative approach, simulating economies as systems of heterogeneous, interacting agents—such as firms and households with and adaptive behaviors—emerging macro patterns from micro-level decisions without presupposing equilibrium. Empirical applications demonstrate ABMs' competitive forecasting accuracy; a 2020 study developed an ABM for European economies that outperformed (VAR) and DSGE benchmarks in out-of-sample predictions of variables like industrial production and over horizons up to eight quarters. Central banks including the have explored ABMs to capture dynamics and business cycles, where traditional models struggle with phenomena like fat-tailed distributions in returns. Despite their utility, economic simulations face inherent limitations rooted in simplifying assumptions that diverge from real-world causal mechanisms, such as neglecting financial frictions or amplifying non-linear feedback loops. DSGE models, reliant on linear approximations around steady states, largely failed to anticipate the , underestimating the systemic risks from subprime mortgage proliferation and leverage buildup due to incomplete incorporation of banking sector dynamics. Post-crisis evaluations highlight how these models' emphasis on representative agents and efficient markets overlooked heterogeneity and contagion effects, leading to over-optimistic stability predictions; for example, pre-2008 simulations projected minimal spillovers from housing corrections. ABMs mitigate some issues by endogenously generating crises through agent interactions but require extensive calibration to data, raising concerns over and computational demands. Overall, while simulations enhance scenario analysis—such as evaluating transmission under interest rate floors—they demand validation against empirical deviations to avoid propagating flawed causal inferences in policy design.

Social Behavior and Urban Planning

Agent-based modeling (ABM) constitutes a primary method for simulating , wherein autonomous agents interact according to predefined rules, yielding emergent macro-level patterns such as segregation, , or in crowds. These models draw on empirical data, including demographic rates and behavioral observations, to parameterize agent decisions, enabling validation against real-world outcomes like residential sorting or . For example, simulations of evacuation scenarios incorporate communication dynamics and tendencies, replicating observed delays in human egress from buildings or events based on data from controlled experiments and historical incidents. In , ABMs extend to forecasting the impacts of , , and changes on and . Platforms like UrbanSim integrate land-use models to evaluate scenarios, such as housing effects on travel patterns, as applied in case studies for cities including and , where simulations informed decisions on by projecting travel times and emissions under alternative growth paths. Activity-based models further simulate individual daily routines—commuting, , and —to assess equity in access to services, revealing disparities in time budgets across socioeconomic groups when calibrated with household travel surveys. Traffic and mobility simulations, often embedded in urban frameworks, model driver and pedestrian behaviors to optimize signal timings and road designs. Large-scale implementations, reviewed across over 60 studies from 23 countries, demonstrate how microsimulations of vehicle interactions reduce congestion by 10-20% in tested networks, validated against sensor data from cities like and . Urban digital twins, combining real-time IoT feeds with behavioral models, support for events like evacuations, where agent rules for route choice and information sharing mirror empirical response times from drills. Such tools prioritize causal linkages, like density's effect on interaction frequency, over aggregate assumptions, though outputs depend on accurate behavioral parameterization from longitudinal datasets.

Critiques of Bias in Social Simulations

Social simulations, encompassing agent-based models (ABMs) and computational representations of human interactions, face critiques for embedding biases that undermine their validity in replicating real-world dynamics. These biases arise from parameterization errors, where parameters calibrated on one population are inappropriately transported to another with differing causal structures, leading to invalid inferences about social outcomes such as disease spread or policy effects. For instance, failure to account for time-dependent confounding in ABMs can amplify collider bias, distorting estimates unless distributions of common causes are precisely known and adjusted. Such issues are particularly acute in social contexts, where heterogeneous behaviors and unmodeled mediators result in simulations that misguide policy decisions by over- or underestimating intervention impacts. A further centers on ideological influences from the political composition of social scientists developing these models. Surveys indicate that 58 to 66 percent of social scientists identify as liberal, with conservatives comprising only 5 to 8 percent, creating an environment where theories and assumptions may systematically favor narratives aligning with left-leaning priors, such as emphasizing systemic inequities over incentives. Honeycutt and Jussim's model posits that this homogeneity manifests in research outputs that flatter liberal values while disparaging conservative ones, potentially embedding similar distortions in simulation assumptions about , inequality persistence, or market responses. Critics argue this skew, exacerbated by institutional pressures in academia, leads to simulations that underrepresent adaptive human behaviors like or voluntary exchange, favoring deterministic or collectivist projections instead. Empirical validation challenges compound this, as multi-agent models often prioritize over external falsification, allowing untested ideological priors to persist. Emerging large language model (LLM)-based social simulations introduce additional layers of bias inherited from training data, including overrepresentation of Western, educated, industrialized, rich, and democratic (WEIRD) populations, resulting in systematic inaccuracies in depicting marginalized groups' behaviors. LLMs exhibit social identity biases akin to humans, with 93 percent more positive sentiment toward ingroups and 115 percent more negative toward outgroups in generated text, which can amplify simulated polarization or conflict beyond empirical realities. In debate simulations, LLM agents deviate from assigned ideological roles by converging toward the model's inherent biases—often left-leaning in perception—rather than exhibiting human-like echo chamber intensification, thus failing to capture genuine partisan divergence on issues like climate policy or gun rights. These flaws highlight the need for rigorous debiasing through diverse data curation and sensitivity testing, though persistent sycophancy in instruction-tuned models risks further entrenching agreeable but unrepresentative social dynamics.

Applications in Defense, Security, and Operations

Military and Tactical Simulations

and tactical simulations involve computer-generated models that replicate environments to train personnel in tactics, weapon systems, and command decisions, minimizing real-world risks and costs associated with live exercises. These systems support individual skills like marksmanship via tools such as the Engagement Skills Trainer II, which simulates live-fire events for crew-served weapons, and collective training at or levels through virtual battlefields. The U.S. invests heavily in such technologies, with unclassified contracts for virtual and augmented simulations totaling $2.7 billion in 2019, reflecting their role in enhancing readiness amid fiscal constraints. Historically, military simulations trace back to ancient wargames using physical models, evolving into computer-based systems by the mid-20th century with networked simulations emerging in the 1960s to model complex warfare dynamics. Modern examples include the Joint Conflict and Tactical Simulation (JCATS), a widely adopted tool across U.S. forces, NATO, and allies for scenario-based tactical exercises involving ground, air, and naval elements. The Marine Corps' MAGTF Tactical Warfare Simulation (MTWS) further exemplifies this by integrating live and simulated forces for staff training at operational levels, while the Navy's AN/USQ-T46 Battle Force Tactical Training (BFTT) coordinates shipboard combat system simulations for team proficiency. Effectiveness studies indicate simulations excel in building foundational skills and scenario repetition, with RAND analyses showing in virtual systems for collective tasks, though they complement rather than replace live due to limitations in replicating physical stressors and unpredictable human factors. Cost-benefit evaluations highlight savings, as simulators amortize procurement expenses quickly compared to live and wear, enabling broader access to high-threat rehearsals. Recent advancements incorporate (VR) and (AI) for greater immersion and adaptability, such as Army VR platforms enhancing gunner protection training through realistic turret interactions and haptic feedback to simulate physical and resistance. AI-driven frameworks enable dynamic enemy behaviors and real-time scenario adjustments, addressing gaps in static models by fostering tactical flexibility in VR/AR environments. These developments, tested in systems like immersive virtual battlefields, prioritize causal accuracy in physics and decision trees to align simulated outcomes with empirical combat data, though validation against historical engagements remains essential to counter over-reliance on abstracted models.

Disaster Response and Risk Assessment

Simulations in involve virtual environments that replicate emergency scenarios to train responders, optimize operational plans, and evaluate coordination among agencies, thereby enhancing preparedness without incurring actual hazards. Agent-based models, for example, simulate individual behaviors in large-scale events, such as comparing immediate evacuation to strategies during floods or earthquakes, revealing that evacuation can overwhelm while sheltering reduces casualties but risks secondary exposures. These models incorporate variables like , , and communication delays, drawing from historical data such as Hurricane Katrina's 2005 evacuation challenges, where simulations post-event identified bottlenecks in interstate capacities exceeding 1 million evacuees. In , probabilistic simulations quantify potential impacts by integrating hazard intensity, vulnerability, and exposure metrics; methods, for instance, run thousands of iterations to estimate loss distributions for , with hurricane models projecting wind speeds up to 200 mph and storm surges of 20 feet causing economic damages exceeding $100 billion in events like in 2017. The U.S. (FEMA) employs such tools under its Homeland Security Exercise and Evaluation Program (HSEEP), established in 2004 and revised in 2020, to conduct discussion-based and operational exercises simulating multi-agency responses to chemical, biological, or radiological incidents, evaluating metrics like response times under 2 hours for urban areas. Global assessments extend this to multi-hazard frameworks, modeling cascading effects like earthquakes triggering tsunamis, with data from events such as the 2011 Tohoku disaster informing models that predict fatality rates varying by building codes and early warning efficacy. High-fidelity simulations, incorporating and real-time data feeds, train medical and first-responder teams in mass-casualty , as demonstrated in exercises replicating surges of 500 patients per hour, improving decision accuracy by 30% over traditional methods per controlled studies. For rural , data-centric tools simulate interactions between limited resources and geographic isolation, such as in wildfires covering 1 million acres, aiding in prepositioning supplies to cut response delays from days to hours. These applications underscore simulations' role in causal chain analysis—from onset to recovery—prioritizing empirical validation against observed outcomes, though models must account for behavioral uncertainties to avoid over-optimism in predictions. Vehicle and equipment simulators further support logistical training, enabling operators to practice in hazardous conditions like debris-strewn roads post-earthquake, with metrics tracking and maneuverability under simulated loads of 20 tons. In , modular simulations integrate forecasts and , as in the Hurricane Evacuation (HUREVAC) model used since the , which processes real-time traffic from 500+ sensors to route 2-3 million people, reducing congestion by clearance times. Peer-reviewed evaluations confirm that such tools enhance equity in distribution by identifying underserved areas, countering biases in from urban-centric historical records.

Manufacturing and Supply Chain Optimization

Simulations in encompass discrete-event modeling, finite element , and to optimize production processes, layout design, and . These tools enable virtual testing of scenarios, reducing physical prototyping costs and time; for instance, a 2021 case study by demonstrated that simulation of a manufacturing line prevented inefficiencies, yielding six-figure annual savings through incremental scenario rather than trial-and-error implementation. —real-time virtual replicas integrated with IoT data—further enhance optimization by predicting equipment failures and streamlining workflows; achieved $11 million in savings and a 40% reduction in unplanned maintenance downtime for components via digital twin applications. In production line management, simulations identify bottlenecks and balance workloads; a study of a mattress manufacturing facility using Arena software revealed opportunities to increase throughput by reallocating resources, though exact gains depended on variable demand inputs. High-mix, low-volume operations benefit from hybrid simulation-optimization approaches, as seen in a 2023 analysis of advanced metal component production, where models minimized setup times and inventory holding costs by integrating stochastic elements like machine breakdowns. Such methods prioritize empirical validation against historical data, avoiding over-reliance on idealized assumptions that could propagate errors in scaling. Supply chain optimization leverages agent-based and simulations to model disruptions, demand variability, and flows, mitigating effects like the phenomenon—where small demand fluctuations amplify upstream. applied simulation to redesign its supply network, reducing inventory levels and variability in lead times through scenario testing of supplier reliability and transportation modes. In automotive contexts, simulation-based material supply models have optimized just-in-time delivery, with one study showing potential reductions in stockouts by up to 30% via shift adjustments during peak periods. Digital twins extend to end-to-end supply chains, enabling what-if analyses for resilience; reports that firms using these technologies achieve inventory reductions of 10-20% and EBITDA improvements by simulating global disruptions like pandemics or tariffs. Peer-reviewed evaluations emphasize causal linkages, such as how modeling of multi-echelon inventories correlates input variances (e.g., supplier delays) to output metrics like service levels, outperforming deterministic heuristics in volatile environments. Despite benefits, simulations require high-fidelity data calibration, as unverified models risk underestimating tail risks in .

Applications in Education, Training, and Entertainment

Educational and Vocational Training Tools

Simulations in and replicate real-world scenarios to facilitate skill development without physical risks or , allowing repeated practice and immediate feedback. These tools have demonstrated effectiveness in enhancing psychomotor skills, , and knowledge retention across disciplines. Peer-reviewed studies indicate that simulation-based approaches outperform traditional methods in vocational contexts by providing immersive, standardized experiences. In training, flight simulators originated with early devices like the 1929 for instrument flight practice, evolving into full-motion systems that integrate analog and digital computing by the mid-20th century. A of simulator training research found consistent performance improvements for jet pilots compared to aircraft-only training, attributing gains to high-fidelity replication of . Medical simulation employs high-fidelity mannequins that mimic physiological responses, such as breathing and cardiac rhythms, enabling trainees to practice procedures like . Systematic reviews of simulation-based learning in education report significant gains in skill acquisition and long-term retention, with effect sizes indicating superior outcomes over lecture-based instruction. These tools reduce harm during novice practice while fostering clinical reasoning. Vocational simulators for industrial trades, including welding systems, cut training costs by eliminating consumables and shorten learning curves; one study documented a 56% reduction in training time alongside improved retention rates. variants provide psychomotor skill transfer comparable to physical , as validated in systematic reviews of VR/AR applications. simulators, such as those for or operation, enable safe hazard recognition and control mastery, essential for certifications in transportation and sectors. Maritime academies utilize bridge simulators to train and response, replicating ship handling under varied conditions to build operational competence. Overall, these tools' efficacy stems from their ability to isolate causal factors in skill-building, though optimal integration requires alignment with learning objectives to maximize transfer to real environments.

Simulation Games and Virtual Reality

Simulation games, also known as sims, are a of video games that replicate aspects of real-world activities or systems, enabling players to engage in , , or operational through abstracted models of physics, , or . Early examples emerged from and academic contexts, with flight simulators developed shortly after powered flight in the early to train pilots without real aircraft risks. The genre gained commercial traction in the 1980s, exemplified by released in 1989 by , which allowed players to build and manage virtual cities, influencing concepts through . Other foundational titles include (1990), focusing on economic of transport networks, and life simulation games like series starting in 2000, which model interpersonal relationships and household dynamics. The integration of (VR) into simulation games has amplified immersion by providing 3D near-eye displays and motion tracking, simulating physical presence in virtual environments. VR enhances applications in racing simulators, such as those using haptic feedback and head-mounted displays to mimic vehicle handling, and games where players interact with scalable models. Developments like the in 2012 spurred VR-specific sim titles, including flight and driving experiences that leverage for realistic spatial awareness. In , VR simulation games facilitate skill-building without physical hazards, as seen in titles testing real-world prototyping and object manipulation. Popular simulation games demonstrate significant market engagement, with the global simulation games segment projected to generate $19.98 billion in revenue in 2025, reflecting a 9.9% annual growth. Titles like Farming Simulator and Kerbal Space Program (launched 2011) have attracted millions of players by balancing accessibility with procedural complexity, the latter praised for its orbital mechanics derived from Newtonian physics. Stardew Valley, a life and farming simulator, exceeded 41 million units sold by December 2024. Despite their appeal, simulation games often prioritize engaging approximations over precise real-world , as models simplify causal interactions like economic feedback loops or physical impacts. Validation studies highlight discrepancies, such as simulators underestimating real impact trajectories due to unmodeled variables like variability. In VR contexts, while immersion aids , inaccuracies in simulated physics can propagate errors in player expectations of , underscoring the need for empirical against observed . This enables broad accessibility but limits utility for high-stakes causal prediction, distinguishing recreational sims from rigorous scientific modeling.

Media Production and Theme Park Experiences

In media production, computer simulations facilitate virtual production workflows by generating real-time environments and effects during filming, minimizing reliance on . For instance, in the Disney+ series (premiered November 12, 2019), Industrial Light & Magic's system employed LED walls displaying Unreal Engine-rendered simulations of planetary landscapes, allowing actors to interact with dynamic, parallax-correct backgrounds lit in real time. This approach, which simulates physical lighting and camera movements computationally, reduced green-screen usage and enabled on-set visualization of complex scenes that would otherwise require extensive CGI layering. Simulations also underpin CGI for modeling physical phenomena in films, such as and particle systems for destruction or weather effects. In James Cameron's Avatar (released December 18, 2009), Weta Digital utilized proprietary to render bioluminescent ecosystems and creature movements, processing billions of procedural calculations to achieve realistic organic behaviors. These techniques, evolved from early applications like the pixelated hand in Westworld (1973), rely on physics-based engines to predict outcomes, enabling directors to iterate shots efficiently before . Previsualization (previs) employs simplified simulations to sequences, particularly for action-heavy productions. Studios like ILM use tools such as Maya or Houdini to simulate camera paths and , as seen in the planning for The Batman (2022), where virtual sets informed practical shoots. In theme park experiences, motion simulators replicate vehicular or adventurous sensations through hydraulic platforms synchronized with projected visuals, originating from training devices patented in the early . The Sanders Teacher, developed in , marked the first motion platform for pilot instruction, evolving into entertainment applications by the 1980s as parks adapted surplus military simulators. Notable implementations include Disney's , launched January 9, 1987, at , which used a six-axis to simulate jumps in a Star Wars-themed starship, accommodating 40 passengers per cycle and generating over 1,000 randomized scenarios via onboard computers. Universal Studios' : The Ride, debuting May 2, 1991, at , featured a 6-degree-of-freedom motion base propelling vehicles through DeLorean time-travel sequences, achieving speeds simulated up to 88 mph while integrating scent and wind effects for immersion. Modern theme park simulators incorporate headsets and advanced haptics; for example, racing simulators at parks like Ferrari World Abu Dhabi (opened October 28, 2010) employ multi-axis gimbals and 200-degree screens to mimic Formula 1 dynamics, drawing from automotive testing tech to deliver G-forces up to 1.5g. These systems prioritize through fail-safes and calibrated feedback loops, distinguishing them from pure gaming by emphasizing shared, large-scale experiential fidelity.

Philosophical Implications

The Simulation Hypothesis

The proposes that what humans perceive as reality is in fact an advanced computer simulation indistinguishable from base physical reality, potentially created by a civilization capable of running vast numbers of such simulations. Philosopher articulated this idea in his 2003 paper "Are You Living in a Computer Simulation?", presenting a : either (1) the human species is likely to become extinct before reaching a stage capable of running high-fidelity simulations; or (2) any civilization is extremely unlikely to run a significant number of such simulations; or (3) the fraction of all observers with human-like experiences that live in simulations is very close to one, implying that our reality is almost certainly simulated. Bostrom's argument hinges on the assumption that posthumans, with immense computational resources, would simulate their evolutionary history for research, entertainment, or other purposes, generating far more simulated conscious beings than exist in any base reality. Bostrom estimates that if posthumans run even a modest number of simulations—say, billions—the probability that an arbitrary observer like a present-day is in base reality drops precipitously, as simulated entities would outnumber non-simulated ones by orders of magnitude. This probabilistic reasoning draws on expected technological in , where simulations could replicate physics at arbitrary given sufficient power, potentially leveraging or other advances to model consciousness and causality. Proponents, including , have popularized the idea; Musk argued in a 2016 Code Conference appearance that, assuming any ongoing rate of technological improvement akin to video games advancing from in 1972 to near-photorealistic graphics by 2016, the odds of living in base reality are "one in billions," as advanced civilizations would produce countless indistinguishable simulations. Despite its logical structure, the hypothesis rests on speculative premises without empirical verification, including the feasibility of simulating consciousness, the motivations of posthumans, and the absence of detectable simulation artifacts like computational glitches or rendering limits. Critics contend it violates Occam's razor by introducing an unnecessary layer of complexity—a simulator—without explanatory power beyond restating observed reality, and it remains unfalsifiable, as any evidence could be dismissed as part of the simulation itself. For instance, assumptions about posthuman interest in ancestor simulations overlook potential ethical prohibitions, resource constraints, or disinterest in historical recreations, rendering the trilemma's third prong probabilistically indeterminate rather than compelling. Moreover, the argument is self-undermining: if reality is simulated, the computational and physical laws enabling the hypothesis's formulation—including probabilistic modeling and technological forecasting—may themselves be artifacts, eroding trust in the supporting science. No direct observational tests exist, though some physicists have proposed seeking inconsistencies in physical constants or quantum measurements as indirect probes, yielding null results to date.

Empirical and Theoretical Debates

The , as formalized by philosopher in his paper, posits a : either nearly all civilizations at our technological level go extinct before reaching a "posthuman" stage capable of running vast numbers of ancestor simulations; or posthumans have little interest in executing such simulations; or we are almost certainly living in one, given that simulated realities would vastly outnumber base ones. This argument relies on assumptions about future technological feasibility, including the ability to simulate conscious minds at the neuronal level with feasible computational resources, and the ethical or motivational incentives of advanced societies to prioritize historical recreations over other pursuits. Critics contend that these premises overlook fundamental barriers, such as the immense energy and hardware demands for simulating an entire down to quantum details, potentially rendering widespread ancestor simulations improbable even for posthumans. Theoretical debates center on the argument's probabilistic structure and hidden priors. Bostrom's expected fraction of simulated observers assumes equal weighting across the trilemma's branches, but detractors argue for adjusting probabilities based on inductive evidence from our universe's apparent base-level physics, where no simulation artifacts (like discrete rendering glitches or resource optimization shortcuts) have been detected at macroscopic scales. Philosopher defends the as compatible with epistemic realism, noting that if simulated, our beliefs about the world remain largely accurate within the program's parameters, avoiding . However, others highlight self-defeating implications: accepting the undermines in the scientific progress enabling simulations, as simulated agents might lack the "true" computational substrate for reliable inference. A 2021 analysis frames the argument's persuasiveness as stemming from narrative immersion rather than deductive soundness, akin to tropes that anthropomorphize advanced simulators without causal grounding in observed reality. Empirically, the hypothesis lacks direct verification, as proposed tests—such as probing for computational shortcuts in distributions or quantum measurement anomalies—yield null results or require unproven assumptions about simulator efficiency. classifies it as , arguing it invokes unobservable programmers to explain observables better accounted for by parsimonious physical laws, without predictive power or . Recent claims, like Melvin Vopson's 2023 proposal of an "infodynamics" law linking information entropy decreases to simulation optimization, remain speculative and unconfirmed by independent replication, relying on reinterpretations of biological and physical data rather than novel experiments. Attempts to derive evidence from fine-tuned constants or holographic principles falter, as these phenomena align equally well with or inflationary models grounded in testable . Overall, the absence of empirical signatures, combined with the hypothesis's dependence on unextrapolated , positions it as philosophically intriguing but evidentially weak compared to causal accounts rooted in observed dynamics.

Causal Realism and First-Principles Critiques

Critics invoking causal realism argue that the introduces superfluous layers of causation without explanatory gain, as observed physical laws—such as the deterministic unfolding of or the probabilistic outcomes in —function with irreducible efficacy that a derivative computational substrate cannot authentically replicate without collapsing into the base mechanisms it emulates. This perspective posits that genuine causation, evidenced by repeatable experiments like particle collisions at the yielding decays on July 4, 2012, demands ontological primacy rather than programmed approximation, rendering the an multiplication of causal agents that fails to resolve empirical regularities. , a theoretical physicist, contends that simulating the universe's quantum many-body dynamics would require resolving chaotic sensitivities and exponential state spaces, infeasible under known computational bounds like the Bekenstein limit on information density, which caps storable bits per volume at approximately 10^69 per cubic meter for a solar-mass . First-principles analysis dismantles the hypothesis by interrogating its core premises: the feasibility of posthuman simulation rests on extrapolating current computational paradigms to godlike scales, yet thermodynamic constraints, including Landauer's principle establishing a minimum energy dissipation of kT ln(2) joules per bit erasure at temperature T (about 2.8 × 10^-21 J at room temperature), imply that emulating a reality-spanning system would dissipate heat exceeding the simulated universe's energy budget. Nick Bostrom's 2003 trilemma—that either civilizations extinct before simulation capability, abstain from running ancestor simulations, or we are likely simulated—presupposes uniform posthuman behavior and ignores the base-reality anchor, where no simulation occurs, aligning with Occam's razor by minimizing assumptions about unobserved nested realities. This deconstruction highlights the argument's reliance on unverified probabilistic ancestry, as the chain of simulators demands an unsimulated terminus, probabilistically favoring a singular base over infinite proliferation without evidence of truncation protocols. The self-undermining nature further erodes the hypothesis: deriving its computational predicates from simulated physics—such as trends observed up to 2025—invalidates those predicates if the simulation alters underlying rules, severing the evidential chain and rendering the conclusion circular. Empirical absence of detectable artifacts, like discrete pixelation at Planck scales (1.6 × 10^-35 meters) or simulation-induced glitches in cosmic microwave background data from Planck satellite observations in 2013, supports direct realism over contrived indirection, as no verified instances of controlled simulations scale to universal fidelity without fidelity loss. Thus, these critiques prioritize verifiable causal chains and parsimonious foundations over speculative ontologies.

Limitations, Criticisms, and Risks

Validation Challenges and Error Propagation

Validation of computational simulations involves assessing whether a model accurately represents the physical phenomena it intends to simulate, typically by comparing outputs to empirical from experiments or observations, while verification ensures the numerical correctly solves the underlying equations. According to ASME standards, validation requires dedicated experiments designed to isolate model predictions, but such experiments often face practical limitations in replicating real-world conditions exactly. High-quality validation remains scarce for complex systems, as real-world measurements can include uncontrolled variables, inaccuracies, or incomplete coverage of spaces. Key challenges include uncertainty in model parameters, where small variations in inputs—such as material properties or boundary conditions—can lead to divergent outcomes, complicating direct comparisons with sparse empirical benchmarks. In fields like (CFD), validation struggles with discrepancies arising from experimental uncertainties, geometric simplifications, or assumptions that do not fully capture chaotic behaviors. Programming errors, inadequate mesh convergence, and failure to enforce conservation laws further undermine credibility, as these introduce artifacts not present in physical systems. Absent universal methodologies, validation often relies on case-specific approaches, risking overconfidence in models tuned to limited datasets rather than broadly predictive ones. Error propagation exacerbates these issues, as numerical approximations—such as from or rounding in —accumulate across iterative steps, potentially amplifying initial inaccuracies exponentially in nonlinear or simulations. In multistep processes, like finite element analysis or integrations, perturbations in early-stage inputs propagate forward, with sensitivity heightened in systems exhibiting instability, such as weather or financial models where minute initial differences yield markedly different long-term results. (UQ) techniques, including sampling or analytical propagation via partial derivatives, attempt to bound these effects by estimating output variances from input distributions, though computational expense limits their application in high-dimensional models. Failure to account for propagation can result in unreliable predictions, as seen in designs where unquantified errors lead to shortfalls or risks.

Overreliance in Policy and Science

Overreliance on in policymaking has been exemplified by the , where compartmental models like the framework projected catastrophic outcomes under unmitigated spread scenarios. In March 2020, the model estimated up to 510,000 deaths in the and 2.2 million in the without interventions, influencing decisions for stringent lockdowns across multiple nations. These projections, however, overestimated fatalities by factors of 10 to 100 in many jurisdictions due to assumptions of homogeneous mixing and static reproduction numbers (R0 around 2.4-3.9), which failed to account for real-world heterogeneities in contact patterns, voluntary behavioral changes, and cross-immunity from prior coronaviruses. Critics, including epidemiologists, noted that such models prioritized worst-case scenarios over probabilistic ranges, leading to policies with substantial economic costs—estimated at trillions globally—while actual excess deaths in lockdown-adopting countries like the totaled around 100,000 by mid-2021, far below projections absent any . In climate policy, general circulation models (GCMs) underpinning agreements like the 2015 Paris Accord have driven commitments to net-zero emissions by 2050 in over 130 countries, yet these models exhibit systematic errors in simulating key processes. For instance, combined uncertainties in cloud feedbacks, water vapor, and aerosol effects yield errors exceeding 150 W/m² in top-of-atmosphere energy balance, over 4,000 times the annual anthropogenic forcing of 0.036 W/m² from CO2 doubling. Observational data from satellites and ARGO buoys since 2000 show tropospheric warming rates 30-50% below GCM ensemble means, with models overpredicting by up to 2.5 times in the tropical mid-troposphere. This discrepancy arises from parameterized sub-grid processes lacking empirical tuning to rare extreme events, fostering overconfidence in high-emissions scenarios (e.g., RCP8.5) that inform trillions in green infrastructure investments, despite their implausibility given coal phase-outs in China and India by 2023. Scientific overreliance manifests in fields like and fluid simulations, where unvalidated approximations propagate errors into downstream applications. In predictions, early simulations using simplified force fields overestimated stability by 20-50% compared to experimental data, delaying pipelines until empirical corrections via in 2020. in has similarly led to redesigns in 10-15% of projects due to model failures under high-Reynolds conditions, as grid resolution limits (often 10^6-10^9 cells) cannot capture chaotic instabilities without ad hoc damping. Such issues underscore the risk of treating simulations as oracles rather than generators, particularly when or hinges on outputs detached from causal validation against physical experiments. In both domains, epistemic pitfalls include in parameter selection and underreporting of sensitivity analyses, amplifying flawed assumptions into authoritative forecasts.

Ethical Concerns and Termination Risks

Ethical concerns surrounding advanced simulations, particularly those posited in the , center on the moral status of simulated entities and the responsibilities of their creators. If simulations replicate conscious experiences indistinguishable from biological ones, creators would bear for any inflicted , such as historical events involving , , or moral atrocities, mirroring ethical obligations toward non-simulated beings. This raises questions about the permissibility of generating realities fraught with empirically observed hardships, including widespread , conflict, and , without consent from the simulated participants. Researchers argue that equating simulated to real implies duties to minimize harm, potentially prohibiting simulations that replicate unethical human behaviors or evolutionary cruelties unless justified by overriding values. The creation of potentially sentient simulated beings also invokes debates over and . For instance, if emulations or artificial minds emerge with subjective experiences, their "deletion" or simulation shutdown could constitute ethical violations comparable to ending organic lives, demanding frameworks for consideration based on and capacity for welfare. from current computational models, such as behaviors mimicking distress signals in training, underscores the need for caution, as scaling to full-brain emulation—projected feasible by some estimates before 2100—amplifies these issues without clear precedents for granting legal or ethical protections. Critics from first-principles perspectives contend that assuming simulated minds lack full weight risks underestimating causal impacts, given indistinguishable phenomenology, though skeptics counter that computational substrates inherently preclude true . Termination risks, a of existential threats tied to simulation science, encompass the potential abrupt cessation of a simulated by its operators. Under the ancestor-simulation framework, posthumans running vast numbers of historical recreations face incentives to halt underperforming or resource-intensive runs, exposing simulated civilizations to shutdown unrelated to their internal progress—evidenced by the trilemma's implication that short-lived simulations dominate due to computational . Bostrom identifies this as a discrete existential : external decisions, such as reallocating hardware or ethical reevaluations, could terminate the simulation at any point, with no recourse for inhabitants. Pursuing empirical tests of the exacerbates these risks, as experiments detecting "glitches" or resource constraints—such as proposed analyses of distributions or quantum measurement anomalies—might prompt simulators to intervene or abort to preserve secrecy or avoid computational overload. Analyses indicate that such probes carry asymmetric dangers, as negative results (confirming base ) provide no disconfirmation utility, while positive signals could trigger defensive shutdowns, a concern amplified by the hypothesis's probabilistic structure favoring simulated over unsimulated observers. For base civilizations, sustaining simulations introduces reciprocal hazards, including resource exhaustion from exponential sim counts or "simulation probes" where aware descendants attempt base-reality breaches, potentially destabilizing the host through unintended causal chains. These risks underscore causal realism's emphasis: simulations do not negate underlying physical constraints, where unchecked proliferation could precipitate civilizational collapse via overcomputation.

Recent Developments and Future Directions

AI-Driven and Cloud-Based Advances

AI integration into simulation workflows has accelerated computational efficiency by employing machine learning models as surrogates for traditional physics-based solvers, reducing runtimes from hours or days to seconds in applications such as computational fluid dynamics and finite element analysis. For instance, physics-informed neural networks (PINNs) embed governing equations directly into neural architectures, enabling rapid approximations of complex phenomena while preserving physical consistency, as demonstrated in engineering designs where AI models trained on high-fidelity simulation data facilitate real-time interactive analysis. In structural mechanics, Ansys leverages NVIDIA's GPU acceleration to refine solvers, achieving up to 10x speedups in multiphysics simulations through parallel processing of large datasets. Similarly, Siemens' Simcenter employs AI for gear stress analysis, combining physics models with machine learning to predict fatigue in under 10 minutes, compared to days for conventional methods. Cloud-based platforms have democratized access to high-performance computing for simulations, allowing distributed processing of massive datasets without on-premises hardware investments. The global cloud-based simulation applications market, valued at $6.3 billion in 2023, is projected to reach $12.3 billion by 2030, driven by demand for scalable, cost-effective solutions in industries like aerospace and automotive. Platforms such as Ansys Cloud and AWS integrate elastic resources for handling petabyte-scale simulations, enabling collaborative workflows where teams run parametric studies across thousands of virtual machines. This shift supports hybrid AI-simulation pipelines, where cloud infrastructure trains deep learning models on simulation outputs, as seen in NVIDIA's frameworks for AI-powered computer-aided engineering (CAE), which deploy inference on distributed GPUs for near-instantaneous design iterations. The convergence of AI and cloud technologies fosters adaptive simulations, incorporating real-time data assimilation for predictive modeling in dynamic environments. In 2025 trends, AI-supported cloud simulations emphasize bidirectional integration with CAD tools and industrial metaverses, enhancing virtual prototyping accuracy while minimizing physical testing. Altair's AI-powered engineering tools exemplify this by embedding 3D simulations into efficient 1D system-level analyses on cloud backends, optimizing resource allocation for sectors like mechanical engineering where iterative testing demands rapid feedback loops. These advances, however, rely on validated training data to mitigate approximation errors, underscoring the need for hybrid approaches blending AI efficiency with deterministic physics validation.

Digital Twins and Multi-Physics Integration

Digital twins integrate multi-physics simulations to create virtual replicas of physical assets that capture interactions across domains such as , , thermal effects, and electromagnetics, enabling and optimization. This approach relies on disparate physical models to simulate real-world behaviors accurately, with updating the twin in real time for fidelity. For instance, in applications, NASA's paradigm employs integrated multiphysics models to represent vehicle systems probabilistically, incorporating multiscale phenomena from material microstructure to system-level performance. Multi-physics integration addresses limitations of single-domain simulations by modeling coupled effects, such as fluid-structure interactions in turbulent flows or thermo-mechanical stresses in processes. In additive , multiscale-multiphysics models simulate powder bed fusion by linking microstructural evolution, , and residual stresses, serving as surrogates for digital twins to predict part quality without extensive physical testing. Similarly, battery digital twins use coupled electrochemical, , and mechanical models to forecast degradation under operational loads, as demonstrated in simulations for packs. Recent advances from 2020 to 2025 emphasize real-time capabilities through edge computing and high-fidelity solvers, reducing latency in multi-physics digital twins for applications like vehicle-to-grid systems, where models predict energy flows integrating electrical, thermal, and behavioral dynamics. In fusion energy research, digital twins incorporate plasma physics with structural and electromagnetic simulations to optimize reactor designs, highlighting challenges in validation and computational scaling. These developments, supported by software like Ansys Twin Builder, have enabled industrial adoption, with examples including engine fleet monitoring where twins simulate wear and failures at rates matching physical counterparts. However, achieving causal accuracy requires rigorous uncertainty quantification to mitigate error propagation across coupled domains.

Prospects for Quantum and High-Fidelity Simulation

Quantum simulation leverages quantum computers to model quantum mechanical systems that are computationally infeasible for classical supercomputers, offering prospects for breakthroughs in materials science, chemistry, and high-energy physics. Recent demonstrations, such as Google Quantum AI's 65-qubit processor achieving a 13,000-fold speedup over the Frontier supercomputer in simulating a complex physics problem, highlight early advantages in specific tasks like random circuit sampling and quantum many-body dynamics. These NISQ-era devices, while noisy, enable analog or variational quantum simulations of phenomena like quark confinement or molecular interactions, with fidelity improvements driven by advanced control techniques, such as MIT's fast pulse methods yielding record gate fidelities exceeding 99.9% in superconducting qubits. Achieving high-fidelity simulations requires scalable error correction to suppress decoherence, paving the way for fault-tolerant quantum computing capable of emulating larger systems with arbitrary precision. IBM's roadmap targets large-scale fault-tolerant systems by 2029 through modular architectures and error-corrected logical qubits, potentially enabling simulations of industrially relevant molecules or condensed matter phases. Quantinuum's accelerated plan aims for universal fault tolerance by 2030 via trapped-ion scaling, emphasizing hybrid quantum-classical workflows for iterative refinement. Innovations like fusion-based state preparation demonstrate scalability for eigenstate generation in quantum simulations, reducing resource overhead for high-fidelity outputs in models up to dozens of qubits. Persistent challenges include coherence times, , and the exponential resource demands for error correction, necessitating millions of physical qubits for practical utility. Algorithmic techniques have shown potential to reduce error-correction overhead by up to 100-fold, accelerating timelines but not eliminating the need for cryogenic and precise . Broader high-fidelity simulations in physics face validation hurdles, as multi-scale phenomena demand coupled models verified against sparse experimental data, with quantum approaches offering complementary insights into regimes like heavy-ion collisions where classical limits persist. Optimistic projections place chemically accurate simulations within reach by 2035–2040, contingent on sustained exceeding $10 billion annually, though hype in vendor roadmaps warrants scrutiny against empirical scaling laws.

References

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