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On the potential of a multi-fidelity G-POD based approach for optimization & uncertainty quantification

On the potential of a multi-fidelity G-POD based approach for optimization & uncertainty quantification
On the potential of a multi-fidelity G-POD based approach for optimization & uncertainty quantification
Traditional multi-fidelity surrogate models require that the output of the low fidelity model be reasonably well correlated with the high fidelity model and will only predict scalar responses. The following paper explores the potential of a novel multi-fidelity surrogate modelling scheme employing Gappy Proper Orthogonal Decomposition (G-POD) which is demonstrated to accurately predict the response of the entire computational domain thus improving optimization and uncertainty quantification performance over both traditional single and multi-fidelity surrogate modelling schemes
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68

Toal, David J.J. (2014) On the potential of a multi-fidelity G-POD based approach for optimization & uncertainty quantification. ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, Dusseldorf, Germany.

Record type: Conference or Workshop Item (Paper)

Abstract

Traditional multi-fidelity surrogate models require that the output of the low fidelity model be reasonably well correlated with the high fidelity model and will only predict scalar responses. The following paper explores the potential of a novel multi-fidelity surrogate modelling scheme employing Gappy Proper Orthogonal Decomposition (G-POD) which is demonstrated to accurately predict the response of the entire computational domain thus improving optimization and uncertainty quantification performance over both traditional single and multi-fidelity surrogate modelling schemes

Text
GT2014-25184 (On the Potential of A Multi-Fidelity G-POD Based Approach for Optimization & Uncertainty Quantification).pdf - Accepted Manuscript
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More information

Published date: 16 June 2014
Venue - Dates: ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, Dusseldorf, Germany, 2014-06-15
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 363184
URI: http://eprints.soton.ac.uk/id/eprint/363184
PURE UUID: dda14a75-1b4a-4c88-a1de-2133c0dbc884
ORCID for David J.J. Toal: ORCID iD orcid.org/0000-0002-2203-0302

Catalogue record

Date deposited: 24 Mar 2014 09:18
Last modified: 10 Jul 2020 00:30

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