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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 -
Sparse polynomial chaos expansions for uncertainty quantification and sensitivity analysis
(2019)Computational models are used nowadays in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. These so-called simulators usually feature dozens of parameters and are expensive to run, even when taking full advantage of the available computer power. In this respect, uncertainty quantification techniques used to solve reliability, sensitivity, model calibration/inversion ...Other Conference Item -
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Adaptive sparse polynomial chaos expansions: A survey
(2019)Book of Abstracts of the 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2019) February 18-22, 2019, Vienna, AustriaOther Conference Item -
Extension of Polynomial Chaos Expansions to the Metamodeling of Stochastic Simulators
(2022)Other Conference Item -
Stochastic spectral likelihood embedding for the calibration of heat transfer models
(2021)Other Conference Item -
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Active learning methods for structural reliability analysis and optimal design
(2023)Other Conference Item -
An introduction to surrogate modelling for uncertainty quantification in computational sciences
(2023)Computational models are nowadays used in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. These models, a.k.a. simulators allow engineers to assess the performance of a system in-silico, and then optimize its design or operating. Simulators such as high-fidelity finite element models usually feature dozens of parameters and are costly to run, even when taking full ...Other Conference Item -
Autoregressive surrogate models of high-dimensional time-dependent wind turbine simulations for uncertainty quantification
(2022)Global adoption of wind as a renewable energy source has increased dramatically over the past decades. With the growing number of wind turbines being installed worldwide, safety concerns and the need to reduce turbine installation and maintenance costs, especially in large wind farms, have become more and more relevant. Wind turbine design is primarily driven by climatic conditions such as wind speed, turbulence, and for offshore ...Other Conference Item