Bayesian sparse polynomial chaos expansion for global sensitivity analysis

被引：95

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 50 条

[1] On the use of sparse Bayesian learning-based polynomial chaos expansion for global reliability sensitivity analysis
Bhattacharyya, Biswarup
[J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2023, 420
[2] Global Sensitivity Analysis for Islanded Microgrid Based on Sparse Polynomial Chaos Expansion
Wang H.
Yan Z.
Xu X.
He K.
[J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2019, 43 (10): : 44 - 52
[3] Structural reliability analysis by a Bayesian sparse polynomial chaos expansion
Bhattacharyya, Biswarup
[J]. STRUCTURAL SAFETY, 2021, 90
[4] Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions
Shao, Qian
Gao, Enlai
Mara, Thierry
Hu, Heng
Liu, Tong
Makradi, Ahmed
[J]. APPLIED ENERGY, 2020, 260 (260)
[5] Reactive Transport Parameter Estimation and Global Sensitivity Analysis Using Sparse Polynomial Chaos Expansion
N. Fajraoui
T. A. Mara
A. Younes
R. Bouhlila
[J]. Water, Air, & Soil Pollution, 2012, 223 : 4183 - 4197
[6] Reactive Transport Parameter Estimation and Global Sensitivity Analysis Using Sparse Polynomial Chaos Expansion
Fajraoui, N.
Mara, T. A.
Younes, A.
Bouhlila, R.
[J]. WATER AIR AND SOIL POLLUTION, 2012, 223 (07): : 4183 - 4197
[7] Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis
Cao, Lixiong
Liu, Jie
Jiang, Chao
Liu, Guangzhao
[J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 399
[8] Global Sensitivity Analysis for Regional Integrated Electricity and Gas System Based on Sparse Polynomial Chaos Expansion
Hu X.
Zhao X.
Feng X.
[J]. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2020, 35 (13): : 2805 - 2816
[9] Global sensitivity analysis using sparse grid interpolation and polynomial chaos
Buzzard, Gregery T.
[J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2012, 107 : 82 - 89
[10] An expanded sparse Bayesian learning method for polynomial chaos expansion
Zhou, Yicheng
Lu, Zhenzhou
Cheng, Kai
Shi, Yan
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 128 : 153 - 171

← 1 2 3 4 5 →