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Proper orthogonal decomposition & kriging strategies for design

Proper orthogonal decomposition & kriging strategies for design
Proper orthogonal decomposition & kriging strategies for design
The proliferation of surrogate modelling techniques have facilitated the application of expensive, high fidelity simulations within design optimisation. Taking considerably fewer function evaluations than direct global optimisation techniques, such as genetic algorithms, surrogate models attempt to construct a surrogate of an objective function from an initial sampling of the design space. These surrogates can then be explored and
updated in regions of interest.

Kriging is a particularly popular method of constructing a surrogate model due to its ability to accurately represent complicated responses whilst providing an error estimate of the predictor. However, it can be prohibitively expensive to construct a kriging model at high dimensions with a large number of sample points due to the cost associated with
the maximum likelihood optimisation.

The following thesis aims to address this by reducing the total likelihood optimisation
cost through the application of an adjoint of the likelihood function within a hybridised optimisation algorithm and the development of a novel optimisation strategy employing
a reparameterisation of the original design problem through proper orthogonal decomposition.
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

Toal, David J.J. (2009) Proper orthogonal decomposition & kriging strategies for design. University of Southampton, School of Engineering Sciences, Doctoral Thesis, 187pp.

Record type: Thesis (Doctoral)

Abstract

The proliferation of surrogate modelling techniques have facilitated the application of expensive, high fidelity simulations within design optimisation. Taking considerably fewer function evaluations than direct global optimisation techniques, such as genetic algorithms, surrogate models attempt to construct a surrogate of an objective function from an initial sampling of the design space. These surrogates can then be explored and
updated in regions of interest.

Kriging is a particularly popular method of constructing a surrogate model due to its ability to accurately represent complicated responses whilst providing an error estimate of the predictor. However, it can be prohibitively expensive to construct a kriging model at high dimensions with a large number of sample points due to the cost associated with
the maximum likelihood optimisation.

The following thesis aims to address this by reducing the total likelihood optimisation
cost through the application of an adjoint of the likelihood function within a hybridised optimisation algorithm and the development of a novel optimisation strategy employing
a reparameterisation of the original design problem through proper orthogonal decomposition.

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More information

Published date: October 2009
Organisations: University of Southampton

Identifiers

Local EPrints ID: 72023
URI: http://eprints.soton.ac.uk/id/eprint/72023
PURE UUID: 581e4f58-3bd8-4d15-87bc-bdacaf988cbe
ORCID for David J.J. Toal: ORCID iD orcid.org/0000-0002-2203-0302

Catalogue record

Date deposited: 15 Jan 2010
Last modified: 30 Jan 2020 01:35

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Contributors

Author: David J.J. Toal ORCID iD
Thesis advisor: A.J. Keane

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