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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 -
Vine copulas for uncertainty quantification: why and how
(2019)Systems subject to uncertain inputs produce uncertain responses. Uncertainty quantification (UQ) deals with the estimation of the response statistics of systems for which a runnable computational model is available. Problems of interest are those where the computational model is expensive, making Monte Carlo approaches unfeasible and thus calling for cheaper solutions that require fewer runs. In these settings, an accurate representation ...Other Conference Item -
A new moment-independent measure for reliability-sensitivity analysis
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A two-stage surrogate modelling approach for the approximation of models with non-smooth outputs
(2019)Other Conference Item -
Literature survey and benchmarking of sparse polynomial chaos expansions
(2019)Other Conference Item -
Surrogate modelling meets machine learning
(2019)Complex computational models are used nowadays in all fields of applied sciences to predict the behaviour of natural, economic and engineering systems. High-fidelity simulators are able to capture more and more realistic features by including multi-scale or multi-physics aspects in their governing equations, which can result in high complexity. Although computer power has attained unprecedented levels, it is still not possible to use brute ...Other Conference Item -
Use of generalized lambda distributions to emulate stochastic simulators
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Combining machine learning and surrogate modeling for data-driven uncertainty propagation in high-dimension
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An adaptive algorithm based on spectral likelihood expansion for efficient Bayesian calibration
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Quantification d'incertitudes en simulation, métamodèles et optimisation fiable
(2019)Other Conference Item