Adaptive explicit decision functions for probabilistic design and optimization using support vector machines

Anirban Basudhar, Samy Missoum

Research output: Contribution to journalArticle

144 Scopus citations

Abstract

This article presents a methodology to generate explicit decision functions using support vector machines (SVM). A decision function is defined as the boundary between two regions of a design space (e.g., an optimization constraint or a limit-state function in reliability). The SVM-based decision function, which is initially constructed based on a design of experiments, depends on the amount and quality of the training data used. For this reason, an adaptive sampling scheme that updates the decision function is proposed. An accurate approximated explicit decision functions is obtained with a reduced number of function evaluations. Three problems are presented to demonstrate the efficiency of the update scheme to explicitly reconstruct known analytical decision functions. The chosen functions are the boundaries of disjoint regions of the design space. A convergence criterion and error measure are proposed. The scheme is also applied to the definition of an explicit failure region boundary in the case of the buckling of a geometrically nonlinear arch.

Original languageEnglish (US)
Pages (from-to)1904-1917
Number of pages14
JournalComputers and Structures
Volume86
Issue number19-20
DOIs
StatePublished - Oct 1 2008

Keywords

  • Discontinuities
  • Disjoint failure regions
  • Explicit decision functions
  • Optimization
  • Probabilistic design
  • Support vector machines

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Materials Science(all)
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Adaptive explicit decision functions for probabilistic design and optimization using support vector machines'. Together they form a unique fingerprint.

  • Cite this