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Efficient Probabilistic Analysis and Design Optimization Using Data Classification Decision Boundaries

[+] Author Affiliations
Liping Wang, Arun K. Subramaniyan

GE Global Research Center, Niskayuna, NY

Don Beeson

GE Aviation, Cincinnati, OH

Paper No. IMECE2010-39921, pp. 161-171; 11 pages
doi:10.1115/IMECE2010-39921
From:
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 11: New Developments in Simulation Methods and Software for Engineering Applications; Safety Engineering, Risk Analysis and Reliability Methods; Transportation Systems
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4448-9
  • Copyright © 2010 by ASME

abstract

A new technique for performing probabilistic analysis and optimization design using data classification methods is investigated. The approach is based on nonlinear decision boundaries constructed from data classification methods. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. An adaptive sampling technique is used to generate samples and update the approximated decision function. The proposed approach is demonstrated with several benchmark and engineering problems.

Copyright © 2010 by ASME
Topics: Design , Optimization

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