%0 Journal Article
%A Azzi, Soumaya
%A Huang, Yuanyuan
%A Sudret, Bruno
%A Wiart, Joe
%D 2019
%I Begell House
%K uncertainty quantification, metamodel, stochastic processes, Karhunen-Loeve expansion, Dosimetry, path loss exponent
%N 4
%P 351-363
%R 10.1615/Int.J.UncertaintyQuantification.2019029103
%T SURROGATE MODELING OF STOCHASTIC FUNCTIONS−APPLICATION TO COMPUTATIONAL ELECTROMAGNETIC DOSIMETRY
%U http://dl.begellhouse.com/journals/52034eb04b657aea,5a3895a14afb242f,2d7a11f63d5a726e.html
%V 9
%X This paper is dedicated to the surrogate modeling of a particular type of computational model called stochastic simulators, which inherently contain some source of randomness. In this particular case the output of the simulator in a given point is a probability density function. In this paper, the stochastic simulator is represented as a stochastic process and the surrogate model is built using the Karhunen-Loeve expansion. In a first approach, the stochastic process covariance was surrogated using polynomial chaos expansion; meanwhile in a second approach the eigenvectors were interpolated. The performance of the method is illustrated on a toy example and then on an electromagnetic dosimetry example. We then provide metrics to measure the accuracy of the surrogate.
%8 2019-08-02