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Stochastic spectral likelihood embedding for the calibration of heat transfer models
(2021)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 -
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 -
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 -
Benchmark of active learning methods for structural reliability analysis
(2022)Other Conference Item -
Dimensionality reduction and surrogate modelling for high-dimensional UQ problems
(2017)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
(2019)Other Conference Item -
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A cost-aware and sensitivity-based active learning algorithm for system reliability
(2022)The safety of structures is generally assessed using structural reliability analysis within a probabilistic framework. The parameters describing the system, which are affected by uncertainties, are represented by random variables and a set of so-called limit-state functions determine whether the system fails. The goal of the analysis is then to estimate the probability of failure of the system. Many techniques have been developed to ...Other Conference Item