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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
(2019)Other Conference Item -
Combining machine learning and surrogate modeling for data-driven uncertainty propagation in high-dimension
(2019)Other Conference Item -
An adaptive algorithm based on spectral likelihood expansion for efficient Bayesian calibration
(2019)Other Conference Item -
Quantification d'incertitudes en simulation, métamodèles et optimisation fiable
(2019)Other Conference Item -
Representation and Inference of Complex dependencies through copulas in UQLab
(2019)Other Conference Item -
Surrogate models for uncertainty quantification and design optimization
(2019)Nowadays computational models are used in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. Also known as simulators, they allow the engineer to assess the performance of a system in-silico, and then optimize its design or operating. Realistic models (e.g. finite element models) usually feature tens of parameters and are costly to run, even when taking full advantage ...Other Conference Item -
Data-based sparse polynomial chaos expansions: applications in dynamics and machine learning
(2019)The links between uncertainty quantification and classical machine learning (ML) have tightened in the past few years. On the one hand, uncertainty propagation techniques are nowadays mainly based on the use of surrogate models such as polynomial chaos expansions (PCE), Gaussian processes or low-rank tensor representations that are constructed in a non-intrusive manner using a batch of computer experiments and dedicated algorithms. On the ...Other Conference Item -
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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