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Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
(2019)Global sensitivity analysis aims at quantifying the impact of input variables (taken separately or as a group) onto the variation of the response of a computational model. Classically, such models (also called simulators) are deterministic, in the sense that repeated runs provide the same output quantity of interest. In contrast, stochastic simulators return different results when run twice with the same input values due to additional ...Other Conference Item -
Extension of Polynomial Chaos Expansions to the Metamodeling of Stochastic Simulators
(2022)Other Conference Item -
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Metamodels of stochastic simulators using polynomial chaos expansions with latent variables
(2021)Other Conference Item -
Construction of sparse polynomial chaos surrogate models for simulators with mixed continuous and categorical variables
(2021)Performing uncertainty quantification for engineering systems typically requires a large number of evaluations of the associated simulation model. Due to limited computational resources, however, such an analysis becomes intractable for expensive numerical models. In this respect, surrogate models have received tremendous attention in the last decade, as they allow one to approximate the original model by an easy-to-evaluate proxy built ...Other Conference Item -
Use of generalized lambda distributions to emulate stochastic simulators
(2019)Other Conference Item