System Simulation Task

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A System Simulation Task is an information processing task that requires the reproduction of some system's behavior over time, typically through computational models.



  • (Wikipedia, 2023) ⇒ Retrieved:2023-6-13.
    • A simulation is the imitation of the operation of a real-world process or system over time. 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. 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, [1] 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.

      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.


  • (Wikipedia, 2023) ⇒ Retrieved:2023-6-13.
    • 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 (some circles[2] use the term for computer simulations modelling selected laws of physics, but this article does not). These physical objects are often chosen because they are smaller or cheaper than the actual object or system.
    • 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.[3]
    • Discrete-event simulation studies systems whose states change their values only at discrete times.[4] 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.[3]
    • 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.[5]
    • 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).[6]
    • Parallel simulation speeds up a simulation's execution by concurrently distributing its workload over multiple processors, as in High-Performance Computing.[7]
    • Interoperable simulation is where multiple models, simulators (often defined as federates) interoperate locally, distributed over a network; a classical example is High-Level Architecture.[8][9]
    • Modeling and simulation as a service is where simulation is accessed as a service over the web.[10]
    • Modeling, interoperable simulation and serious games is where serious game approaches (e.g. game engines and engagement methods) are integrated with interoperable simulation.[11]
    • 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.Template:Refn
    • 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.
  1. In the words of the Simulation article in Encyclopedia of Computer Science, "designing a model of a real or imagined system and conducting experiments with that model".
  2. For example in computer graphics SIGGRAPH 2007 | For Attendees | Papers Doc:Tutorials/Physics/BSoD – BlenderWiki Template:Webarchive.
  3. 3.0 3.1 McLeod, J. (1968) "Simulation: the Dynamic Modeling of Ideas And Systems with Computers", McGraw-Hill, NYC.
  4. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000) "Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems", Elsevier, Amsterdam.
  5. Giambiasi, N., Escude, B., & Ghosh, S. (2001). GDEVS: A generalized discrete event specification for accurate modeling of dynamic systems. In Autonomous Decentralized Systems, 2001. Proceedings. 5th International Symposium on (pp. 464–469). IEEE.
  6. Petty, M. D. (April 1995). Computer-generated forces in a distributed interactive simulation. In Distributed Interactive Simulation Systems for Simulation and Training in the Aerospace Environment: A Critical Review (Vol. 10280, p. 102800I). International Society for Optics and Photonics.
  7. Fujimoto, R. M. (1990). Parallel discrete event simulation. Communications of the ACM, 33(10), 30–53.
  8. Kuhl, F., Weatherly, R., & Dahmann, J. (1999). Creating computer simulation systems: an introduction to the high-level architecture. Prentice Hall PTR.
  9. Bruzzone A.G., Massei M., Simulation-Based Military Training, in Guide to Simulation-Based Disciplines, Vol.1. 315–361.
  10. Cayirci, E. (December 2013). Modeling and simulation as a cloud service: a survey. In Simulation Conference (WSC), 2013 Winter (pp. 389–400). IEEE.
  11. Bruzzone, A. G., Massei, M., Tremori, A., Longo, F., Nicoletti, L., Poggi, S., ... & Poggio, G. (2014). MS2G: simulation as a service for data mining and crowdsourcing in vulnerability Reduction. Proceedings of WAMS, Istanbul, September.