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BY-NC-ND 4.0 license Open Access Published by De Gruyter 2021

10 Kriging: methods and applications

From the book Volume 1 System- and Data-Driven Methods and Algorithms

  • Jack P. C. Kleijnen


In this chapter we present Kriging-also known as a Gaussian process (GP) model-which is a relatively simple metamodel-or emulator or surrogate-of the corresponding complex simulation model. To select the input combinations to be simulated, we use Latin hypercube sampling (LHS); these combinations may have uniform and non-uniform distributions. Besides deterministic simulation we discuss random-or stochastic-simulation, which requires adjusting the design and analysis. We discuss sensitivity analysis of simulation models, using “functional analysis of variance” (FANOVA)-also known as Sobol sensitivity indices. Finally, we discuss optimization of the simulated system, including “robust” optimization.

© 2021 Walter de Gruyter GmbH, Berlin/Munich/Boston
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