Local update of support vector machine decision boundaries

Anirban Basudhar, Samy Missoum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


This paper presents a new adaptive sampling technique for the construction of locally refined explicit decision functions. The decision functions can be used for both deterministic and probabilistic optimization, and may represent a constraint or a limit-state function. In particular, the focus of this paper is on reliability-based design optimization (RBDO). Instead of approximating the responses, the method is based on explicit design space decomposition (EDSD), in which an explicit boundary separating distinct regions in the design space is constructed. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. A major advantage of using an EDSD-based method lies in its ability to handle discontinuous responses. A separate adaptive sampling scheme for calculating the probability of failure is also developed, which is used within the RBDO process. The update methodology is validated through several test examples with analytical decision functions.

Original languageEnglish (US)
Title of host publication17th AIAA/ASME/AHS Adaptive Structures Conf., 11th AIAA Non-Deterministic Approaches Conf., 10th AIAA Gossamer Spacecraft Forum, 5th AIAA Multidisciplinary Design Optimization Specialist Conf., MDO
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781563479731
StatePublished - 2009

Publication series

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

ASJC Scopus subject areas

  • Architecture
  • General Materials Science
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Local update of support vector machine decision boundaries'. Together they form a unique fingerprint.

Cite this