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- W2088880006 abstract "Sensitivity analysis aims at quantifying influence of input parameters dispersion on the output dispersion of a numerical model. When the model evaluation is time consuming, the computation of Sobol' indices based on Monte Carlo method is not applicable and a surrogate model has to be used. Among all approximation methods, polynomial chaos expansion is one of the most efficient to calculate variance-based sensitivity indices. Indeed, their computation is analytically derived from the expansion coefficients but without error estimators of the meta-model approximation. In order to evaluate the reliability of these indices, we propose to build confidence intervals by bootstrap re-sampling on the experimental design used to estimate the polynomial chaos approximation. Since the evaluation of the sensitivity indices is obtained with confidence intervals, it is possible to find a design of experiments allowing the computation of sensitivity indices with a given accuracy." @default.
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- W2088880006 date "2014-01-01" @default.
- W2088880006 modified "2023-10-17" @default.
- W2088880006 title "Construction of bootstrap confidence intervals on sensitivity indices computed by polynomial chaos expansion" @default.
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- W2088880006 doi "https://doi.org/10.1016/j.ress.2013.09.011" @default.
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