A methodology for fitting and validating metamodels in simulation

The methodological developments and statistical computing details which make this approach efficient are described in detail.Illustrations of our model are given for both synthetic and real datasets.There are several types of metamodel: linear regression, splines, neural networks, etc.This paper distinguishes between fitting and validating a metamodel.By continuing to use this site, you consent to the use of cookies.We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.Several validation criteria, measures, and estimators are discussed.

To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in lieu of the actual simulation models.

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.

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.

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