Paper: Nov 21,2019
stat.CO
ID:1911.09067
Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
Due to limited computational power, performing uncertainty quantification
analyses with complex computational models can be a challenging task. This is
exacerbated in the context of stochastic simulators, the response of which to a
given set of input parameters, rather than being a deterministic value, is a
random variable with unknown probability density function (PDF). Of interest in
this paper is the construction of a surrogate that can accurately predict this
response PDF for any input parameters. We suggest using a flexible distribution
family -- the generalized lambda distribution -- to approximate the response
PDF. The associated distribution parameters are cast as functions of input
parameters and represented by sparse polynomial chaos expansions. To build such
a surrogate model, we propose an approach based on a local inference of the
response PDF at each point of the experimental design based on replicated model
evaluations. Two versions of this framework are proposed and compared on
analytical examples and case studies.
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Paper Author: X. Zhu,B. Sudret
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