Structural optimization using femlab and smooth support vector regression

Bo Ping Wang*, Divija Odapally, Yuh-Jye Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

An effective algorithm for structural optimization is proposed in this paper. In the proposed method, the optimum design is achieved sequentially based on the surrogate model constructed by smooth support vector regression (SSVR). The proposed research work uses Quasi Monte Carlo (QMC) technique for the selection of training data in the design space. SSVR using a radial basis function kernel is used to build the metamodel for structural optimization. The structural responses are evaluated by a commercial finite element package, FEMLAB (recently renamed as COMSOL). Several examples are presented to illustrate the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationCollection of Technical Papers - 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
Pages2568-2577
Number of pages10
DOIs
StatePublished - 6 Aug 2007
Event48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference - Waikiki, HI, United States
Duration: 23 Apr 200726 Apr 2007

Publication series

NameCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Volume3
ISSN (Print)0273-4508

Conference

Conference48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
CountryUnited States
CityWaikiki, HI
Period23/04/0726/04/07

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