RT Journal Article ID 17fb6a69404aa954 A1 Zhu, Xujia A1 Sudret, Bruno T1 REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS JF International Journal for Uncertainty Quantification JO IJUQ YR 2020 FD 2020-06-19 VO 10 IS 3 SP 249 OP 275 K1 stochastic simulators K1 surrogate modeling K1 generalized lambda distributions K1 sparse polynomial chaos expansions AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,5047a058688097ba,17fb6a69404aa954.html