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Uncertainties assessment in global sensitivity indices estimation from metamodels

Alexandre Janon 1, 2, * Maëlle Nodet 1 Clémentine Prieur 1, 2
* Corresponding author
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Global sensitivity analysis is often impracticable for complex and resource intensive numerical models, as it requires a large number of runs. The metamodel approach replaces the original model by an approximated code that is much faster to run. This paper deals with the information loss in the estimation of sensitivity indices due to the metamodel approximation. A method for providing a robust error assessment is presented, hence enabling significant time savings without sacrificing on precision and rigor. The methodology is illustrated on two different types of metamodels: one based on reduced basis, the other one on RKHS interpolation.
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Alexandre Janon, Maëlle Nodet, Clémentine Prieur. Uncertainties assessment in global sensitivity indices estimation from metamodels. International Journal for Uncertainty Quantification, Begell House Publishers, 2014, 4 (1), pp.21-36. ⟨10.1615/Int.J.UncertaintyQuantification.2012004291⟩. ⟨inria-00567977v3⟩



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