{"id":{"repo_id":"soton","oai_identifier":"oai:eprints.soton.ac.uk:72023"},"canonical_url":"https://search.dev.ndltd.org/etd/soton/oai:eprints.soton.ac.uk:72023","repository":{"repo_id":"soton","name":"University of Southampton","base_url":"https://eprints.soton.ac.uk/cgi/oai2"},"display":{"title":"Proper orthogonal decomposition & kriging strategies for design","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.","abstract_html":"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<br/>updated in regions of interest.<br/><br/>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<br/>the maximum likelihood optimisation.<br/><br/>The following thesis aims to address this by reducing the total likelihood optimisation<br/>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<br/>a reparameterisation of the original design problem through proper orthogonal decomposition.","abstract_has_math":false,"creators":["Toal, David J.J."],"institution":"University of Southampton","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Keane, A.J."],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-10","date_published":"2009-10","updated_at":"2026-07-24T04:36:06Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Keane, A.J."]},{"key":"dc:creator","label":"Author","values":["Toal, David J.J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2009-10"]},{"key":"dc:date.issued","label":"Date","values":["2009-10"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Computational Engineering and Design (pre 2011 reorg)","School of Engineering Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Southampton"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://eprints.soton.ac.uk/72023/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Ph.D."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://eprints.soton.ac.uk/72023/1/Toal_thesis.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Proper orthogonal decomposition & kriging strategies for design"]}]}],"canonical_facts":{"dc:contributor.advisor":["Keane, A.J."],"dc:creator":["Toal, David J.J."],"dc:date":["2009-10"],"dc:date.issued":["2009-10"],"dc:description.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."],"dc:format":["text"],"dc:identifier.uri":["https://eprints.soton.ac.uk/72023/1/Toal_thesis.pdf"],"dc:publisher.department":["Computational Engineering and Design (pre 2011 reorg)","School of Engineering Sciences"],"dc:publisher.institution":["University of Southampton"],"dc:relation.isreferencedby":["https://eprints.soton.ac.uk/72023/"],"dc:title":["Proper orthogonal decomposition & kriging strategies for design"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["Ph.D."]},"updated_at":"2026-07-24T04:36:06Z"}