Methods for high-dimensional and computationally intensive models

M. Balesdent, L. Brevaul, S. Lacaze, S. Missoum, J. Morio

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Complex simulation codes such as the ones used in aerospace industry are often computationally expensive and involve a large number of variables. These features significantly hamper the estimation of rare event probabilities. To reduce the computational burden, an analysis of the most important variables of the problem can be performed before applying rare event estimation methods. Another way to reduce this burden is to build a surrogate model of the computationally costly simulation code and to perform the probability estimation on this metamodel. In this chapter, we first review the main techniques used in sensitivity analysis and then describe several surrogate models that are efficient in the probability estimation context.

Original languageEnglish (US)
Title of host publicationEstimation of Rare Event Probabilities in Complex Aerospace and Other Systems
Subtitle of host publicationA Practical Approach
PublisherElsevier Inc.
Pages109-136
Number of pages28
ISBN (Electronic)9780081001110
ISBN (Print)9780081000915
DOIs
StatePublished - Jan 1 2016

Keywords

  • ANOVA
  • Kriging
  • Morris method
  • Sensitivity analysis
  • Sobol
  • Support vector machines
  • Surrogate model

ASJC Scopus subject areas

  • Engineering(all)

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    Balesdent, M., Brevaul, L., Lacaze, S., Missoum, S., & Morio, J. (2016). Methods for high-dimensional and computationally intensive models. In Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems: A Practical Approach (pp. 109-136). Elsevier Inc.. https://doi.org/10.1016/B978-0-08-100091-5.00008-3