Bayesian sparse polynomial chaos expansion for global sensitivity analysis

Qian Shao 1, 2 A Younes 3, 4 M Fahs 3 Thierry A. Mara 5, 2
1 School of Civil Engineering
2 PIMENT - Physique et Ingénierie Mathématique pour l'Énergie, l'environnemeNt et le bâtimenT
3 LHyGeS - Laboratoire d'Hydrologie et de Géochimie de Strasbourg
4 UMR LISAH - Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème
5 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques
Abstract : 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.
Keywords : Double-diffusive convection Kashyap information criterion Global sensitivity analysis Sobol' indices Sparse polynomial chaos expansion Bayesian model averaging
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Article dans une revue
Computer Methods in Applied Mechanics and Engineering, Elsevier, 2017, 318, pp.474-496. 〈10.1016/j.cma.2017.01.033〉
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Soumis le : samedi 25 février 2017 - 11:10:42
Dernière modification le : mardi 29 mai 2018 - 12:50:52

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Qian Shao, A Younes, M Fahs, Thierry A. Mara. Bayesian sparse polynomial chaos expansion for global sensitivity analysis. Computer Methods in Applied Mechanics and Engineering, Elsevier, 2017, 318, pp.474-496. 〈10.1016/j.cma.2017.01.033〉. 〈hal-01476649〉

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