An estimation algorithm for fast kriging surrogates of computer models with unstructured multiple outputs

Computationally intensive computer models are used in many areas of engineering. In order to speed up the investigations, fast statistical surrogates have been developed in the literature. The surrogates addressed in this paper incorporate a general and unstructured covariance, best suited for model...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 321; pp. 35 - 45
Main Author: Drignei, Dorin
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2017
Elsevier BV
Subjects:
Algorithms
Computer based modeling
Computer experiments
Covariance
Emulators
Kriging
Maximum likelihood estimation
Mechanical engineering
Multidimensional data
Prediction
Multidimensional data
Computer experiments
Emulators
Prediction
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:Computationally intensive computer models are used in many areas of engineering. In order to speed up the investigations, fast statistical surrogates have been developed in the literature. The surrogates addressed in this paper incorporate a general and unstructured covariance, best suited for modeling nonlinear and nonstationary multiple outputs. We propose an efficient algorithm to cope with the estimation of a large number of parameters. Then multivariate kriging is used to construct the fast surrogate. This algorithm can be embedded in both maximum likelihood and cross-validation estimation methods. We compare the proposed method with a current method based on principal components. The methodology is illustrated with a mechanical engineering application involving a vehicle suspension system.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2017.04.001