Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions

Evgeny Burnaev, Ivan Panin, Bruno Sudret

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)

    Abstract

    Global sensitivity analysis aims at quantifying respective effects of input random variables (or combinations thereof) onto variance of a physical or mathematical model response. Among the abundant literature on sensitivity measures, Sobol indices have received much attention since they provide accurate information for most of models. We consider a problem of experimental design points selection for Sobol’ indices estimation. Based on the concept of D-optimality, we propose a method for constructing an adaptive design of experiments, effective for calculation of Sobol’ indices based on Polynomial Chaos Expansions. We provide a set of applications that demonstrate the efficiency of the proposed approach.

    Original languageEnglish
    Pages (from-to)187-207
    Number of pages21
    JournalAnnals of Mathematics and Artificial Intelligence
    Volume81
    Issue number1-2
    DOIs
    Publication statusPublished - 1 Oct 2017

    Keywords

    • Active learning
    • Design of experiment
    • Polynomial chaos expansions
    • Sensitivity analysis
    • Sobol indices

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