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Blind Kriging: implementation and performance analysis

Blind Kriging: implementation and performance analysis
Blind Kriging: implementation and performance analysis
When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly differing in the type of regression function used. Recently a promising new variable selection technique was proposed to identify a regression function in the Kriging framework. In this paper this type of Kriging, i.e., blind Kriging, has been efficiently implemented in Matlab (R) and has been extended. The implementation is validated and tested on several examples to illustrate the strength and weaknesses of this new, promising modelling technique. It is shown that the performance of blind Kriging is as good as, or better than ordinary Kriging. Though, blind Kriging comes at double the computational cost with respect to ordinary Kriging. (C) 2012 Elsevier Ltd. All rights reserved.
0965-9978
1-13
Couckuyt, I.
6738ed2d-69b7-45ad-977a-d46546afd336
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Gorissen, Dirk
9ab3c73b-f111-4230-9571-74fe48628a5d
De Turck, F.
d8b8c021-432d-4225-93a9-d07b884999b4
Dhaene, T.
fa910ec1-ca25-44e0-aa7f-26b1434fc10b
Couckuyt, I.
6738ed2d-69b7-45ad-977a-d46546afd336
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Gorissen, Dirk
9ab3c73b-f111-4230-9571-74fe48628a5d
De Turck, F.
d8b8c021-432d-4225-93a9-d07b884999b4
Dhaene, T.
fa910ec1-ca25-44e0-aa7f-26b1434fc10b

Couckuyt, I., Forrester, A.I.J., Gorissen, Dirk, De Turck, F. and Dhaene, T. (2012) Blind Kriging: implementation and performance analysis. Advances in Engineering Software, 49, 1-13. (doi:10.1016/j.advengsoft.2012.03.002).

Record type: Article

Abstract

When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly differing in the type of regression function used. Recently a promising new variable selection technique was proposed to identify a regression function in the Kriging framework. In this paper this type of Kriging, i.e., blind Kriging, has been efficiently implemented in Matlab (R) and has been extended. The implementation is validated and tested on several examples to illustrate the strength and weaknesses of this new, promising modelling technique. It is shown that the performance of blind Kriging is as good as, or better than ordinary Kriging. Though, blind Kriging comes at double the computational cost with respect to ordinary Kriging. (C) 2012 Elsevier Ltd. All rights reserved.

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Published date: July 2012
Organisations: Faculty of Engineering and the Environment

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Local EPrints ID: 364898
URI: http://eprints.soton.ac.uk/id/eprint/364898
ISSN: 0965-9978
PURE UUID: 24d44c39-deec-4db8-807f-41e34983d818

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Date deposited: 15 May 2014 13:39
Last modified: 16 Jul 2019 21:05

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Contributors

Author: I. Couckuyt
Author: Dirk Gorissen
Author: F. De Turck
Author: T. Dhaene

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