RT Journal Article ID 2d7a11f63d5a726e A1 Azzi, Soumaya A1 Huang, Yuanyuan A1 Sudret, Bruno A1 Wiart, Joe T1 SURROGATE MODELING OF STOCHASTIC FUNCTIONS−APPLICATION TO COMPUTATIONAL ELECTROMAGNETIC DOSIMETRY JF International Journal for Uncertainty Quantification JO IJUQ YR 2019 FD 2019-08-02 VO 9 IS 4 SP 351 OP 363 K1 uncertainty quantification K1 metamodel K1 stochastic processes K1 Karhunen-Loeve expansion K1 Dosimetry K1 path loss exponent AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,5a3895a14afb242f,2d7a11f63d5a726e.html