Probabilistic optimal design in the presence of random fields

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21 Scopus citations


This article describes a methodology to incorporate a random field in a probabilistic optimization problem. The approach is based on the extraction of the features of a random field using a reduced number of experimental observations. This is achieved by proper orthogonal decomposition. Using Lagrange interpolation, a modified random field is obtained by changing the contribution of each feature. The contributions are controlled using scalar parameters, which can be considered as random variables. This allows one to perform a random-field-based probabilistic optimization with few random variables. The methodology is demonstrated on a tube impacting a rigid wall for which a random field modifies the planarity of the tube's wall.

Original languageEnglish (US)
Pages (from-to)523-530
Number of pages8
JournalStructural and Multidisciplinary Optimization
Issue number6
StatePublished - Jun 2008


  • Probabilistic optimal design
  • Proper orthogonal decomposition
  • Random fields

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization


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