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

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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Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2017, 〈10.1007/s10472-017-9542-1〉
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Soumis le : jeudi 3 août 2017 - 12:11:00
Dernière modification le : vendredi 17 novembre 2017 - 13:22:01

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Evgeny Burnaev, Ivan Panin, Bruno Sudret. Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions. Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2017, 〈10.1007/s10472-017-9542-1〉. 〈hal-01571681〉

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