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{{Short description|Experiment used to study computer simulation}}
A '''computer experiment''' or '''simulation experiment''' is an experiment used to study a computer simulation, also referred to as an [[in silico]] system. This area includes [[computational physics]], [[computational chemistry]], [[computational biology]] and other similar disciplines.
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[[Computer simulation]]s are constructed to emulate a physical system. Because these are meant to replicate some aspect of a system in detail, they often do not yield an analytic solution. Therefore, methods such as [[discrete event simulation]] or [[finite element]] solvers are used. A [[computer model]] is used to make inferences about the system it replicates. For example, [[climate models]] are often used because experimentation on an earth sized object is impossible.
==Objectives==
Computer experiments have been employed with many purposes in mind. Some of those include:
* [[Uncertainty quantification]]: Characterize the uncertainty present in a computer simulation arising from unknowns during the computer simulation's construction.
* [[Inverse problem]]s: Discover the underlying properties of the system from the physical data.
* Bias correction: Use physical data to correct for bias in the simulation.
* [[Data assimilation]]: Combine multiple simulations and physical data sources into a complete predictive model.
* [[Systems design]]: Find inputs that result in optimal system performance measures.
==Computer simulation modeling==
Modeling of computer experiments typically uses a Bayesian framework. [[Bayesian statistics]] is an interpretation of the field of [[statistics]] where
Although <math>f(\cdot)</math> is known in principle, in practice this is not the case. Many simulators comprise tens of thousands of lines of high-level computer code, which is not accessible to intuition. For some simulations, such as climate models,
===Gaussian process prior===
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==Design of computer experiments==
The design of computer experiments has considerable differences from [[design of experiments]] for parametric models. Since a Gaussian process prior has an infinite dimensional representation, the concepts of A and D criteria (see [[Optimal design]]), which focus on reducing the error in the parameters, cannot be used. Replications would also be wasteful in cases when the computer simulation has no error. Criteria that are used to determine a good experimental design include integrated mean squared prediction error [
Popular strategies for design include [[latin hypercube sampling]] and [[low discrepancy sequences]].
===Problems with massive sample sizes===
Unlike physical experiments, it is
==See also==
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*[[Gaussian process emulator]]
*[[Design of experiments]]
*[[Molecular dynamics]]
*[[Monte Carlo method]]
*[[Surrogate model]]
*[[Grey box completion and validation]]
*[[Artificial financial market]]
==Further reading==
* {{cite book | last = Santner | first = Thomas | title = The Design and Analysis of Computer Experiments | publisher = Springer | ___location = Berlin | year = 2003 | isbn = 0-387-95420-1 }}
* {{cite journal | last1 = Fehr | first1 = Jörg | last2 = Heiland | first2 = Jan | last3 = Himpe | first3 = Christian | last4 = Saak | first4 = Jens | title = Best practices for replicability, reproducibility and reusability of computer-based experiments exemplified by model reduction software | journal = AIMS Mathematics | volume = 1 | issue = 3 | pages = 261–281 | date = 2016 | doi = 10.3934/Math.2016.3.261 | arxiv = 1607.01191 | s2cid = 14715031 }}
[[Category:Computational science]]
[[Category:
[[Category:Simulation]]
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