Global sensitivity analysis of computer models with functional inputs

Abstract : Global sensitivity analysis is used to quantify the influence of uncertain input parameters on the response variability of a numerical model. The common quantitative methods are applicable to computer codes with scalar input variables. This paper aims to illustrate different variance-based sensitivity analysis techniques, based on the so-called Sobol indices, when some input variables are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary meta-modeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked Generalized Linear Models (GLM) or Generalized Additive Models (GAM). The ``mean'' model allows to estimate the sensitivity indices of each scalar input variables, while the ``dispersion'' model allows to derive the total sensitivity index of the functional input variables. The proposed approach is compared to some classical SA methodologies on an analytical function. Lastly, the proposed methodology is applied to a concrete industrial computer code that simulates the nuclear fuel irradiation.
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Soumis le : lundi 9 juin 2008 - 00:33:18
Dernière modification le : mercredi 19 février 2020 - 09:22:22
Archivage à long terme le : mardi 21 septembre 2010 - 16:55:40


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  • HAL Id : hal-00243156, version 2
  • ARXIV : 0802.1009



Bertrand Iooss, Mathieu Ribatet. Global sensitivity analysis of computer models with functional inputs. Reliability Engineering and System Safety, Elsevier, 2009, 94 (7), pp.1194-1204. ⟨hal-00243156v2⟩



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