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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 -
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 -
Representation and Inference of Complex dependencies through copulas in UQLab
(2019)Other Conference Item