Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
This work addresses uncertainty quantification for stochastic simulators, which is critical for fields like engineering and computational modeling, but it appears incremental as it builds on existing surrogate modeling techniques.
The paper tackles the challenge of predicting the response probability density function (PDF) of stochastic simulators for uncertainty quantification by constructing a surrogate model using generalized lambda distributions and sparse polynomial chaos expansions, with results demonstrated on analytical examples and case studies.
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.