A knowledge-based approach to response surface modelling in multifidelity optimization

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), 297-319. (doi:10.1023/A:1023283917997).


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Original Publication URL: http://dx.doi.org/10.1023/A:1023283917997


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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1023/A:1023283917997
ISSNs: 0925-5001 (print)
Related URLs:
Keywords: multifidelity modelling, knowledge, based neural networks, kriging, expensive function optimization
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions : University Structure - Pre August 2011 > School of Engineering Sciences
University Structure - Pre August 2011 > School of Electronics and Computer Science
ePrint ID: 22418
Accepted Date and Publication Date:
Date Deposited: 22 Mar 2006
Last Modified: 31 Mar 2016 11:41
URI: http://eprints.soton.ac.uk/id/eprint/22418

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