How to choose the simulation model for computer experiments: a local approach

In many scientific areas, non‐stochastic simulation models such as finite element simulations replace real experiments. A common approach is to fit a meta‐model, for example a Gaussian process model, a radial basis function interpolation, or a kernel interpolation, to computer experiments conducted...

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Vydané v:Applied stochastic models in business and industry Ročník 28; číslo 4; s. 354 - 361
Hlavní autori: Mühlenstädt, Thomas, Gösling, Marco, Kuhnt, Sonja
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 01.07.2012
Predmet:
computer code validation
computer experiment
Gaussian process model
kernel interpolation
sheet metal forming
ISSN:1524-1904, 1526-4025
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Shrnutí:In many scientific areas, non‐stochastic simulation models such as finite element simulations replace real experiments. A common approach is to fit a meta‐model, for example a Gaussian process model, a radial basis function interpolation, or a kernel interpolation, to computer experiments conducted with the simulation model. This article deals with situations where more than one simulation model is available for the same real experiment, with none being the best over all possible input combinations. From fitted models for a real experiment as well as for computer experiments using the different simulation models, a criterion is derived to identify the locally best one. Applying this criterion to a number of design points allows the design space to be split into areas where the individual simulation models are locally superior. An example from sheet metal forming is analyzed, where three different simulation models are available. In this application and many similar problems, the new approach provides valuable assistance with the choice of the simulation model to be used. Copyright © 2011 John Wiley & Sons, Ltd.
Bibliografia:ArticleID:ASMB909
istex:D44AB8761862AA35A2A45FAB84B4AB9AD018CC75
ark:/67375/WNG-B14PSZSF-L
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.909