Generative topographic mapping for dimension reduction in engineering design
Generative topographic mapping for dimension reduction in engineering design
Multi-variate design optimization is plagued by the problem of a design space which increases exponentially with number of variables. The computational burden caused by this 'curse of dimensionality' can be avoided by reducing the dimension of the problem. This work describes a dimension reduction method called generative topographic mapping. Unlike the earlier practice of removing irrelevant design variables for dimension reduction, this method transforms the high dimensional data space to a low dimensional one. Hence there is no risk of missing out on information relating to any variables during the dimension redution. The method is demonstrated using the two dimensional Branin function and applied to a problem in wing design.
9783642137990
204-207
Springer Berlin, Heidelberg
Vishwanath, Asha
b6eb9241-1302-4246-bf1e-23ae2101eec1
Forrester, Alexander I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
January 2010
Vishwanath, Asha
b6eb9241-1302-4246-bf1e-23ae2101eec1
Forrester, Alexander I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
Vishwanath, Asha, Forrester, Alexander I.J. and Keane, Andy J.
(2010)
Generative topographic mapping for dimension reduction in engineering design.
Blum, Christian and Battiti, Roberto
(eds.)
In Learning and Intelligent Optimization: 4th International Conference, LION 4.
Springer Berlin, Heidelberg.
.
(doi:10.1007/978-3-642-13800-3).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Multi-variate design optimization is plagued by the problem of a design space which increases exponentially with number of variables. The computational burden caused by this 'curse of dimensionality' can be avoided by reducing the dimension of the problem. This work describes a dimension reduction method called generative topographic mapping. Unlike the earlier practice of removing irrelevant design variables for dimension reduction, this method transforms the high dimensional data space to a low dimensional one. Hence there is no risk of missing out on information relating to any variables during the dimension redution. The method is demonstrated using the two dimensional Branin function and applied to a problem in wing design.
More information
Published date: January 2010
Venue - Dates:
conference; 2009-01-01, 2010-01-01
Identifiers
Local EPrints ID: 161677
URI: http://eprints.soton.ac.uk/id/eprint/161677
ISBN: 9783642137990
PURE UUID: 9f98c51a-75c9-4d4e-9928-7eba87ea756f
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Date deposited: 04 Aug 2010 08:22
Last modified: 14 Mar 2024 02:39
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Contributors
Author:
Asha Vishwanath
Editor:
Christian Blum
Editor:
Roberto Battiti
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