A methodology for fitting and validating metamodels in simulation
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.
1 Despite the advances in computer capacity, the enormous computational cost of complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization.
There are several types of metamodel: linear regression, splines, neural networks, etc.
This paper distinguishes between fitting and validating a metamodel.
Mixing between full Gaussian processes and simple linear models can yield a more parsimonious spatial model while significantly reducing computational effort.
1 Introduction Simulation-based analysis tools are finding increased use during preliminary design to explore desi...
Citation Context ..kriging method has advantages in that it provides a basis for a stepwise algorithm to determine the important factors, and the same data can be used for screening and building the predictor model (=-=Welch, et al., 1992)-=-.
Several validation criteria, measures, and estimators are discussed.
Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.