Distribution-based global sensitivity analysis using polynomial chaos expansions

Abstract

Uncertainty in the model input parameters are to be taken into account in order to assess the robustness of the model response. Sensitivity analysis based on the variance decomposition (e.g. Sobol’ indices) is relatively expensive because of the conditional moments estimation. In this work, an alternative method for quantifying the uncertainty of the response due to an input variable without any reference to the response moments is applied. In order to minimize the cost of each model evaluation, a metamodel called polynomial chaos expansion is substituted to the initial model. The process is applied to numerical test cases. The results are discussed and compared with the reference obtained with variance-based methods.

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