ANOVA decomposition of conditional Gaussian processes for sensitivity analysis with dependent inputsReport as inadecuate




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1 GdR MASCOT-NUM - Méthodes d-Analyse Stochastique des Codes et Traitements Numériques 2 MOISE - Modelling, Observations, Identification for Environmental Sciences Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble 3 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

Abstract : Complex computer codes are widely used in science to model physical systems. Sensitivity analysis aims to measure the contributions of the inputs on the code output variability. An efficient tool to perform such analysis are the variance-based methods which have been recently investigated in the framework of dependent inputs. One of their issue is that they require a large number of runs for the complex simulators. To handle it, a Gaussian process regression model may be used to approximate the complex code. In this work, we propose to decompose a Gaussian process into a high dimensional representation. This leads to the definition of a variance-based sensitivity measure well tailored for non-independent inputs. We give a methodology to estimate these indices and to quantify their uncertainty. Finally, the approach is illustrated on toy functions and on a river flood model.

Keywords : Gaussian process regression dependent inputs Sensitivity analysis complex computer codes functional decomposition





Author: Gaëlle Chastaing - Loic Le Gratiet -

Source: https://hal.archives-ouvertes.fr/



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