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

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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.

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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Building 3, Nobel st., Moscow, 143026, Russia

    Evgeny Burnaev

  2. Kharkevich Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow, 127994, Russia

    Evgeny Burnaev & Ivan Panin

  3. ETH Zurich, Chair of Risk, Safety and Uncertainty Quantification, Stefano-Franscini-Platz 5, CH-8093, Zurich, Switzerland

    Bruno Sudret

  4. National Research University Higher School of Economics, Myasnitskaya st. 20, Moscow, 109028, Russia

    Evgeny Burnaev

Authors
  1. Evgeny Burnaev
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  2. Ivan Panin
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  3. Bruno Sudret
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Corresponding author

Correspondence to Ivan Panin.

Additional information

The research of E. Burnaev and I. Panin was supported by the Russian Science Foundation grant (project 14-50-00150).

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Burnaev, E., Panin, I. & Sudret, B. Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions. Ann Math Artif Intell 81, 187–207 (2017). https://doi.org/10.1007/s10472-017-9542-1

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  • DOI: https://doi.org/10.1007/s10472-017-9542-1

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