Uncertainties assessment in global sensitivity indices estimation from metamodels

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 precision and rigor. The methodology is illustrated for two different types of metamodels: one based on reduced basis, the other one on reproducing Kernel Hilbert space (RKHS) interpolation.