A Bayesian approach for global sensitivity analysis of (multi-fidelity) computer codes
| Autor: | Le Gratiet, Loic, Cannamela, Claire, Iooss, Bertrand |
|---|---|
| Přispěvatelé: | Laboratoire de Probabilités et Modèles Aléatoires (LPMA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Management des Risques Industriels (EDF R&D MRI), EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
multi-fidelity model
complex computer codes [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] FOS: Mathematics Bayesian analysis Mathematics - Statistics Theory Statistics Theory (math.ST) [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] Sensitivity analysis Sobol index Gaussian process regression |
| Zdroj: | SIAM/ASA Journal on Uncertainty Quantification SIAM/ASA Journal on Uncertainty Quantification, 2014, 2, pp.336-363 |
| ISSN: | 2166-2525 |
| Popis: | International audience; Complex computer codes are widely used in science and engineering to model physical phenomena. Furthermore, it is common that they have a large number of input parameters. Global sensitivity analysis aims to identify those which have the most important impact on the output. Sobol indices are a popular tool to perform such analysis. However, their estimations require an important number of simulations and often cannot be processed under reasonable time constraint. To handle this problem, a Gaussian process regression model is built to surrogate the computer code and the Sobol indices are estimated through it. The aim of this paper is to provide a methodology to estimate the Sobol indices through a surrogate model taking into account both the estimation errors and the surrogate model errors. In particular, it allows us to derive non-asymptotic confidence intervals for the Sobol index estimations. Furthermore, we extend the suggested strategy to the case of multi-fidelity computer codes which can be run at different levels of accuracy. For such simulators, we use an extension of Gaussian process regression models for multivariate outputs. |
| Databáze: | OpenAIRE |
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