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Robust design optimization using surrogate models

Robust design optimization using surrogate models
Robust design optimization using surrogate models
The use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian Process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up Design Search and Optimization (DSO) in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with Uncertainty Quantification (UQ) of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here we tackle the problem of Robust Design Optimization (RDO) from the direction of Gaussian Process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach we term combined coKriging that attempts to make best use of the key ideas present in these methods.
2288-4300
44–55
Keane, Andrew
26d7fa33-5415-4910-89d8-fb3620413def
Voutchkov, Ivan
16640210-6d07-49cc-aebd-28bf89c7ac27
Keane, Andrew
26d7fa33-5415-4910-89d8-fb3620413def
Voutchkov, Ivan
16640210-6d07-49cc-aebd-28bf89c7ac27

Keane, Andrew and Voutchkov, Ivan (2020) Robust design optimization using surrogate models. Journal of Computational Design and Engineering, 7 (1), 44–55. (doi:10.1093/jcde/qwaa005).

Record type: Article

Abstract

The use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian Process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up Design Search and Optimization (DSO) in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with Uncertainty Quantification (UQ) of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here we tackle the problem of Robust Design Optimization (RDO) from the direction of Gaussian Process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach we term combined coKriging that attempts to make best use of the key ideas present in these methods.

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More information

In preparation date: 4 January 2019
Accepted/In Press date: 8 August 2019
e-pub ahead of print date: 19 March 2020
Published date: 2020

Identifiers

Local EPrints ID: 433442
URI: http://eprints.soton.ac.uk/id/eprint/433442
ISSN: 2288-4300
PURE UUID: 1854faa2-f344-4a27-90c6-c0fd1a7b431f

Catalogue record

Date deposited: 22 Aug 2019 16:30
Last modified: 08 Aug 2020 04:01

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