Computer experiment: Difference between revisions

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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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* [[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.

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===Problems with massive sample sizes===

Unlike physical experiments, it is common for computer experiments to have thousands of different input combinations. Because the standard inference requires [[Invertible matrix|matrix inversion]] of a square matrix of the size of the number of samples (<math>n</math>), the cost grows on the <math> \mathcal{O} (n^3) </math>. Matrix inversion of large, dense matrices can also cause numerical inaccuracies. Currently, this problem is solved by greedy decision tree techniques, allowing effective computations for unlimited dimensionality and sample size [httphttps://wwwpatents.google.com/patentspatent/WO2013055257A1?cl=/en&hl=ru patent WO2013055257A1], or avoided by using approximation methods, e.g. [https://wayback.archive-it.org/all/20120130182750/http://www.stat.wisc.edu/~zhiguang/Multistep_AOS.pdf].

==See also==

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*[[Surrogate model]]

*[[Grey box completion and validation]]

*[[Artificial financial market]]

==Further reading==

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* {{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--281261–281 | date = 2016 | doi = 10.3934/Math.2016.3.261 | arxiv = 1607.01191 | s2cid = 14715031 }}

[[Category:Computational science]]