A Methodology for Fitting and Validating Metamodels in Simulation

J.P.C. Kleijnen, R. Sargent

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Abstract

This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages36
Volume1997-116
Publication statusPublished - 1997

Publication series

NameCentER Discussion Paper
Volume1997-116

Keywords

  • Simulation
  • approximation
  • response surface
  • modelling
  • regression

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    Kleijnen, J. P. C., & Sargent, R. (1997). A Methodology for Fitting and Validating Metamodels in Simulation. (CentER Discussion Paper; Vol. 1997-116). Operations research.