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Article Dans Une Revue Annals of Mathematics and Artificial Intelligence Année : 2017

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

Résumé

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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Dates et versions

hal-01571681 , version 1 (03-08-2017)

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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, 2017, ⟨10.1007/s10472-017-9542-1⟩. ⟨hal-01571681⟩

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