Global sensitivity analysis of stochastic computer models with joint metamodels |
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Authors: | Amandine Marrel Bertrand Iooss Sébastien Da Veiga Mathieu Ribatet |
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Institution: | 1.IFP Energies Nouvelles,Rueil-Malmaison,France;2.EDF, R&D,Chatou,France;3.Université Montpellier II,Montpellier,France |
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Abstract: | The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical
model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input
variables always gives the same output value. This paper proposes a global sensitivity analysis methodology for stochastic
computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and
dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint
models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based
upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimators even when
heteroscedasticity is strong. |
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Keywords: | |
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