A knowledge-based approach to response surface modelling in multifidelity optimization
A knowledge-based approach to response surface modelling in multifidelity optimization
This paper is concerned with approximations for expensive function evaluation – the expensive functions arising in an engineering design context. The problem of reducing the computational cost of generating sufficient learning samples is addressed. Several approaches of using a priori knowledge to achieve computational economy are presented. In all these, the results of a cheap model are treated as knowledge to be incorporated in the training process. Several approaches are described here: in particular, we focus on neural based systems. This approach is then developed as a new knowledge-based kriging model which is shown to be as accurate as neural based alternatives while being much easier to train. Examples from the domain of structural optimization are given to demonstrate the approach.
multifidelity modelling, knowledge, based neural networks, kriging, expensive function optimization
297-319
Leary, Stephen J.
2f0f8880-bc29-4d3b-9af8-b66d759e4092
Bhaskar, Atul
d4122e7c-5bf3-415f-9846-5b0fed645f3e
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
2003
Leary, Stephen J.
2f0f8880-bc29-4d3b-9af8-b66d759e4092
Bhaskar, Atul
d4122e7c-5bf3-415f-9846-5b0fed645f3e
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Leary, Stephen J., Bhaskar, Atul and Keane, Andy
(2003)
A knowledge-based approach to response surface modelling in multifidelity optimization.
Journal of Global Optimization, 26 (3), .
(doi:10.1023/A:1023283917997).
Abstract
This paper is concerned with approximations for expensive function evaluation – the expensive functions arising in an engineering design context. The problem of reducing the computational cost of generating sufficient learning samples is addressed. Several approaches of using a priori knowledge to achieve computational economy are presented. In all these, the results of a cheap model are treated as knowledge to be incorporated in the training process. Several approaches are described here: in particular, we focus on neural based systems. This approach is then developed as a new knowledge-based kriging model which is shown to be as accurate as neural based alternatives while being much easier to train. Examples from the domain of structural optimization are given to demonstrate the approach.
Text
lear_03a.pdf
- Accepted Manuscript
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Published date: 2003
Keywords:
multifidelity modelling, knowledge, based neural networks, kriging, expensive function optimization
Identifiers
Local EPrints ID: 22418
URI: http://eprints.soton.ac.uk/id/eprint/22418
ISSN: 0925-5001
PURE UUID: 176438b9-7848-4133-8408-784cbca0674b
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Date deposited: 22 Mar 2006
Last modified: 16 Mar 2024 02:53
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Author:
Stephen J. Leary
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