Stochastic intrinsic Kriging for simulation metamodeling

Stochastic intrinsic Kriging for simulation metamodeling Kriging (or a Gaussian process) provides metamodels for deterministic and random simulation models. Actually, there are several types of Kriging; the classic type is the so‐called universal Kriging, which includes ordinary Kriging. These classic types require estimation of the trend in the input‐output data of the underlying simulation model; this estimation weakens the Kriging metamodel. We therefore consider the so‐called intrinsic Kriging (IK), which originated in geostatistics, and derive IK types for deterministic simulations and random simulations, respectively. Moreover, for random simulations, we derive experimental designs that specify the number of replications that varies with the input combination of the simulation model. To compare the performance of IK and classic Kriging, we use several numerical experiments with deterministic simulations and random simulations, respectively. These experiments show that IK gives better metamodels, in most experiments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Stochastic intrinsic Kriging for simulation metamodeling

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
D.O.I.
10.1002/asmb.2300
Publisher site
See Article on Publisher Site

Abstract

Kriging (or a Gaussian process) provides metamodels for deterministic and random simulation models. Actually, there are several types of Kriging; the classic type is the so‐called universal Kriging, which includes ordinary Kriging. These classic types require estimation of the trend in the input‐output data of the underlying simulation model; this estimation weakens the Kriging metamodel. We therefore consider the so‐called intrinsic Kriging (IK), which originated in geostatistics, and derive IK types for deterministic simulations and random simulations, respectively. Moreover, for random simulations, we derive experimental designs that specify the number of replications that varies with the input combination of the simulation model. To compare the performance of IK and classic Kriging, we use several numerical experiments with deterministic simulations and random simulations, respectively. These experiments show that IK gives better metamodels, in most experiments.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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