Editing Simulation (section)

==Classification and terminology==
[[File:Christer Fuglesang underwater EVA simulation for STS-116.jpg|thumb|Human-in-the-loop simulation of [[outer space]]]]
[[File:Lambda2 scherschicht.png|thumb|right|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. ({{crossref|See also: [[physical model]] and [[scale model]].}})
Alternatively, ''physical simulation'' may refer to computer simulations considering selected laws of physics, as in [[multiphysics simulation]].<ref>For example in [[computer graphics]] [http://www.siggraph.org/s2007/attendees/papers/12.html SIGGRAPH 2007 | For Attendees | Papers] [http://wiki.blender.org/index.php/BSoD/Physical_Simulation Doc:Tutorials/Physics/BSoD – BlenderWiki] {{Webarchive|url=https://web.archive.org/web/20071012202002/http://wiki.blender.org/index.php/BSoD/Physical_Simulation |date=12 October 2007 }}.</ref> ({{crossref|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]], [[Maritime simulator|sailing simulator]], or [[driving simulator]].

''[[Continuous simulation]]'' is a simulation based on [[Discrete time and continuous time|continuous-time rather than discrete-time]] steps, using numerical integration of [[differential equation]]s.<ref name="McLeod, J. 1968">McLeod, J. (1968) "Simulation: the Dynamic Modeling of Ideas And Systems with Computers", McGraw-Hill, NYC.</ref>

''[[Discrete-event simulation]]'' studies systems whose states change their values only at discrete times.<ref>Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000) "Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems", Elsevier, Amsterdam.</ref> 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 method|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.<ref name="McLeod, J. 1968"/>

''[[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.<ref>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.</ref>

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

A '''{{visible anchor|distributed simulation}}'''<!--boldface per [[WP:R#PLA]]--> 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).<ref>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.</ref>

''Parallel simulation'' speeds up a simulation's execution by concurrently distributing its workload over multiple processors, as in [[High-Performance Computing|high-performance computing]].<ref>Fujimoto, R. M. (1990). Parallel discrete event simulation. Communications of the ACM, 33(10), 30–53.</ref>

''Interoperable simulation'' is where multiple models, simulators (often defined as federates) interoperate locally, distributed over a network; a classical example is [[High-Level Architecture]].<ref>Kuhl, F., Weatherly, R., & Dahmann, J. (1999). Creating computer simulation systems: an introduction to the high-level architecture. Prentice Hall PTR.</ref><ref>Bruzzone A.G., Massei M., Simulation-Based Military Training, in Guide to Simulation-Based Disciplines, Vol.1. 315–361.</ref>

''Modeling and simulation as a service'' is where simulation is accessed as a service over the web.<ref>Cayirci, E. (December 2013). Modeling and simulation as a cloud service: a survey. In Simulation Conference (WSC), 2013 Winter (pp. 389–400). IEEE.</ref>

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

''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.{{refn|name="environment"|1=[[Thales Group|Thales]] defines synthetic environment as "the counterpart to simulated models of sensors, platforms and other active objects" for "the simulation of the external factors that affect them"<ref>{{cite web|url=http://www.thalesresearch.com/Default.aspx?tabid=181|website=Thalse|access-date=24 December 2007|url-status=dead|archive-url=https://web.archive.org/web/20060621052423/http://thalesresearch.com/Default.aspx?tabid=181|archive-date=21 June 2006|title= Modelling, Simulation & Synthetic Environments}}</ref> while other vendors use the term for more visual, [[virtual reality]]-style simulators.<ref>{{cite web|title=Synthetic Environments|website=CAE|url= http://www.cae.com/www2004/Products_and_Services/Civil_Simulation_and_Training/Simulation_Equipment/Visual_Solutions/Synthetic_Environments/index.shtml |access-date=24 December 2007|url-status=dead|archive-url= https://web.archive.org/web/20080122060652/http://www.cae.com/www2004/Products_and_Services/Civil_Simulation_and_Training/Simulation_Equipment/Visual_Solutions/Synthetic_Environments/index.shtml|archive-date=22 January 2008}}</ref>}}

''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.