Bayesian sparse polynomial chaos expansion for global sensitivity analysis

被引：107

作者：

Shao, Qian ^{[1
,2
]}

Younes, Anis ^{[3
,4
,5
]}

Fahs, Marwan ^{[3
]}

Mara, Thierry A. ^{[2
]}

机构：

[1] Wuhan Univ, Sch Civil Engn, 8 South Rd East Lake, Wuhan 430072, Peoples R China

[2] Univ Reunion, PIMENT, EA 4518, FST, 15 Ave Rene Cassin, F-97715 St Denis, Reunion, France

[3] Univ Strasbourg, EOST, LHyGeS, UMR CNRS 7517, 1 Rue Blessig, F-67084 Strasbourg, France

[4] IRD UMR LISAH, F-92761 Montpellier, France

[5] Ecole Natl Ingenieurs Tunis, LMHE, Tunis, Tunisia

来源：

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING | 2017年 / 318卷

关键词：

Global sensitivity analysis; Sobol' indices; Sparse polynomial chaos expansion; Bayesian model averaging; Kashyap information criterion; Double diffusive convection; MODEL; REGRESSION; COMPUTATION; CONVECTION; SELECTION;

D O I：

10.1016/j.cma.2017.01.033

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Polynomial chaos expansions are frequently used by engineers and modellers for uncertainty and sensitivity analyses of computer models. They allow representing the input/output relations of computer models. Usually only a few terms are really relevant in such a representation. It is a challenge to infer the best sparse polynomial chaos expansion of a given model input/output data set. In the present article, sparse polynomial chaos expansions are investigated for global sensitivity analysis of computer model responses. A new Bayesian approach is proposed to perform this task, based on the Kashyap information criterion for model selection. The efficiency of the proposed algorithm is assessed on several benchmarks before applying the algorithm to identify the most relevant inputs of a double-diffusive convection model. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：474 / 496

页数：23

共 43 条

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[Anonymous], 2008, GLOBAL SENSITIVITY A

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