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国际不确定性的量化期刊
影响因子: 3.259 5年影响因子: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

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

SURROGATE MODELING OF STOCHASTIC FUNCTIONS−APPLICATION TO COMPUTATIONAL ELECTROMAGNETIC DOSIMETRY

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

ABSTRACT

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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