Simulation technology is the use of computational models, mathematical representations, physical systems, virtual environments or combinations of these techniques to reproduce selected characteristics and behaviour of a real or hypothetical system. A simulation does not necessarily reproduce reality in every detail; instead, it represents the aspects that matter for a particular purpose. A model of an aircraft, for example, may represent aerodynamics, propulsion, control inputs and atmospheric conditions without modelling every molecule of air.
Simulation can represent physical processes, human behaviour, economic systems, traffic, industrial processes, computer networks, weather, spacecraft, medical procedures and many other domains. The central idea is experimentation with a representation of a system so that behaviour can be observed without necessarily manipulating the real system.
Simulation technology has roots in mathematical modelling, physical experimentation and analogue computing, but modern computational simulation expanded substantially with digital computers. Early applications included scientific calculations, military planning, engineering analysis and operations research. As processors, memory, graphics hardware and numerical methods improved, simulations became capable of representing increasingly complex dynamic systems. Real-time graphics later enabled interactive simulations in which users can manipulate a virtual environment and immediately observe consequences. Modern simulation can combine physics engines, numerical solvers, artificial intelligence, sensor models, geographic information, computer graphics, real-world data and distributed computing. The field therefore encompasses far more than visual virtual reality or computer games.
A simulation normally consists of a model, input conditions, rules governing system behaviour, a mechanism for progressing the simulated state and outputs that can be observed or measured. Some simulations are deterministic, meaning identical inputs produce identical results under the same model and numerical conditions. Others are stochastic and incorporate probability distributions or random variables to represent uncertainty. Simulations can be static, examining a system at a particular state, or dynamic, representing changes over time.
They can operate faster than real time for long-term analysis, slower than real time for detailed study, or approximately in real time for training and interactive applications. The choice depends on the purpose, computational resources, required accuracy and temporal characteristics of the system being modelled.
Simulation is widely used because experimentation with real systems can be expensive, dangerous, slow, inaccessible or ethically problematic. Engineers can simulate structures before construction, manufacturers can test production processes before changing machinery, and aerospace organisations can examine flight behaviour without repeatedly performing physical tests. Medical education can use simulated patients and procedures for training. Emergency planners can simulate evacuations or disaster responses. Businesses can model supply chains and demand. Scientists can simulate phenomena that are difficult to observe directly, such as astrophysical processes or molecular interactions. Military and aviation organisations can use simulation for training without exposing personnel to every risk associated with live exercises.
Computer simulation can be classified according to how the underlying system is represented. Discrete-event simulation represents changes occurring at distinct events and is common in queues, logistics and manufacturing. Continuous simulation uses differential equations or other numerical techniques to represent continuously changing variables. Agent-based simulation models individual entities and their interactions, allowing population-level behaviour to emerge from local rules. Monte Carlo simulation repeatedly samples uncertain inputs to estimate distributions, probabilities or expected outcomes. Hybrid simulations combine different approaches. Digital twins extend simulation concepts by maintaining a computational representation of a particular real-world asset or process and potentially updating it with operational data. These categories can overlap, and a sophisticated simulation may combine several methods.
The credibility of a simulation depends on verification, validation and appropriate assumptions. Verification asks whether the computational implementation correctly represents the intended mathematical or logical model. Validation asks whether the model adequately represents the real system for the intended purpose. Calibration can adjust model parameters using observations from the real system. Sensitivity analysis examines how changes in assumptions or inputs affect outputs. Uncertainty analysis distinguishes uncertainty in inputs and parameters from uncertainty or limitations in the model itself. A simulation can produce highly precise numerical results while still being a poor representation of reality if important variables are omitted or assumptions are inappropriate. Computational complexity, numerical instability, incomplete data and incorrect boundary conditions can also affect results.
Simulation technology is closely related to virtual reality, computer graphics, artificial intelligence and game technology but is not synonymous with any of them. A simulation can operate without graphics, such as a numerical climate model, and computer graphics can generate imagery without simulating physical behaviour. A game can contain simulation elements but normally prioritises interaction and entertainment, whereas an engineering simulation may prioritise physical accuracy. Artificial intelligence can be incorporated into simulations to control agents, estimate unknown variables or generate realistic behaviour, while simulations can also provide environments for training AI systems. The distinction is therefore based primarily on purpose and representation rather than the particular software or hardware used.
Modern simulation technology increasingly supports real-time, interactive and data-connected systems. High-performance computing allows large scientific and engineering simulations, while GPUs accelerate many parallel numerical and graphical calculations. Cloud computing can provide scalable computational capacity, and distributed simulation can connect multiple models or participants. Digital twins can combine live sensor information with predictive models. Extended-reality interfaces can allow users to interact with simulations spatially. Simulation is also increasingly used to evaluate autonomous systems, robotics, infrastructure and complex socio-technical processes. Its fundamental limitation remains that a simulation is a model rather than the real system: useful results depend on the quality of the model, data, assumptions, numerical methods and interpretation.
