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International Journal for Uncertainty Quantification
IF: 3.259 5-Year IF: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029103
pages 351-363


Soumaya Azzi
LTCI, Télécom ParisTech, Chair C2M, 46 Rue Barrault, 75013 Paris, France
Yuanyuan Huang
LTCI, Télécom ParisTech, Chair C2M, 46 Rue Barrault, 75013 Paris, France
Bruno Sudret
ETH Zurich, Institute of Structural Engineering, Chair of Risk, Safety and Uncertainty Quantification, Stefano-Franscini-Platz 5, CH-8093 Zurich, Switzerland
Joe Wiart
LTCI, Télécom ParisTech, Chair C2M, 46 Rue Barrault, 75013 Paris, France


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.


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