Simulation in the context of Programming code

Virtual reality (VR) is a simulated experience that employs 3D near-eye displays and pose tracking to give the user an immersive feel of a virtual world. Applications of virtual reality include entertainment (particularly video games), education (such as medical, safety, or military training), research and business (such as virtual meetings). VR is one of the key technologies in the reality-virtuality continuum. As such, it is different from other digital visualization solutions, such as augmented virtuality and augmented reality.

Currently, standard virtual reality systems use either virtual reality headsets or multi-projected environments to generate some realistic images, sounds, and other sensations that simulate a user's physical presence in a virtual environment. A person using virtual reality equipment is able to look around the artificial world, move around in it, and interact with virtual features or items. The effect is commonly created by VR headsets consisting of a head-mounted display with a small screen in front of the eyes but can also be created through specially designed rooms with multiple large screens. Virtual reality typically incorporates auditory and video feedback but may also allow other types of sensory and force feedback through haptic technology.

Special effects (often abbreviated as F/X or simply FX) are illusions or visual tricks used in the theater, film, television, video game, amusement park and simulator industries to simulate the fictional events in a story or virtual world. It is sometimes abbreviated as SFX, but this may also refer to sound effects.

Special effects are traditionally divided into the categories of mechanical effects and optical effects. With the emergence of digital filmmaking a distinction between special effects and visual effects has grown, with the latter referring to digital post-production and optical effects, while "special effects" refers to mechanical effects.

Scientific modelling is an activity that produces models representing empirical objects, phenomena, and physical processes, to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate. It requires selecting and identifying relevant aspects of a situation in the real world and then developing a model to replicate a system with those features. Different types of models may be used for different purposes, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, computational models to simulate, and graphical models to visualize the subject.

Modelling is an essential and inseparable part of many scientific disciplines, each of which has its own ideas about specific types of modelling. The following was said by John von Neumann.

Computer simulation is the running of a mathematical model on a computer, the model being designed to represent the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.

Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling. In 1997, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program.Other examples include a 1-billion-atom model of material deformation; a 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005;a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.

The Stanford prison experiment (SPE), also referred to as the Zimbardo prison experiment (ZPE), was a controversial psychological experiment performed in August 1971 at Stanford University. It was designed to be a two-week simulation of a prison environment that examined the effects of situational variables on participants' reactions and behaviors. Stanford University psychology professor Philip Zimbardo managed the research team who administered the study. Zimbardo ended the experiment early after realizing the guard participants' abuse of the prisoners had gone too far.

Participants were recruited from the local community through an advertisement in the newspapers offering $15 per day ($119.41 in 2025) to male students who wanted to participate in a "psychological study of prison life". 24 participants were chosen after assessments of psychological stability and then assigned randomly to the role of prisoners or prison guards. Critics have questioned the validity of these methods.Those volunteers selected to be "guards" were given uniforms designed specifically to de-individuate them, and they were instructed to prevent prisoners from escaping. The experiment started officially when "prisoners" were arrested by the real police of Palo Alto. During the next five days, psychological abuse of the prisoners by the "guards" became increasingly brutal. After psychologist Christina Maslach visited to evaluate the conditions, she was troubled to see how study participants were behaving and she confronted Zimbardo. He ended the experiment on the sixth day.

A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility.

PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed.

An instruction set simulator (ISS) is a simulation model, usually coded in a high-level programming language, which mimics the behavior of a mainframe or microprocessor by "reading" instructions and maintaining internal variables which represent the processor's registers.

Instruction simulation is a methodology employed for one of several possible reasons:

In business, training simulation (also known as simulation-based training) is a virtual medium through which various types of skills can be acquired. Training simulations can be used in a variety of genres; however they are most commonly used in corporate situations to improve business awareness and management skills. They are also common in academic environments as an integrated part of a business or management course.

The word "simulation" implies an imitation of a real-life process, usually via a computer or other technological device, in order to provide a lifelike experience. This has proven to be a reliable and successful method of training in thousands of industries worldwide. They can be used both to allow specialization in a certain area, and to educate individuals in the workings of the sectors as a whole, making training simulations versatile. Training simulations are not just games; their aim is to educate and inform in an exciting and memorable way, rather than purely to entertain.

Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making.

In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model. The mathematical model represents the physical model in virtual form, and conditions are applied that set up the experiment of interest. The simulation starts – i.e., the computer calculates the results of those conditions on the mathematical model – and outputs results in a format that is either machine- or human-readable, depending upon the implementation.

Game design is the process of creating and shaping the mechanics, systems, rules, and gameplay of a game. Game design processes apply to board games, card games, dice games, casino games, role-playing games, sports, war games, or simulation games.

In Elements of Game Design, game designer Robert Zubek defines game design by breaking it down into three elements:

In physics, a variational principle is an alternative method for determining the state or dynamics of a physical system, by identifying it as an extremum (minimum, maximum or saddle point) of a function or functional. Variational methods are exploited in many modern software applications to simulate matter and light.

Since the development of analytical mechanics in the 18th century, the fundamental equations of physics have usually been established in terms of action principles, where the variational principle is applied to the action of a system in order to recover the fundamental equation of motion.