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 http://dl.begellhouse.com/journals/52034eb04b657aea,5a3895a14afb242f,2d7a11f63d5a726e.html