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Surrogate models for uncertainty quantification in computational sciences
(2022)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. 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 -
Benchmark of active learning methods for structural reliability analysis
(2022)Other Conference Item -
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 -
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 -
Multi-objective robust optimization using adaptive Kriging
(2022)Accounting for uncertainties is crucial in the design of engineering systems. Various techniques have been developed for design optimization within a probabilistic framework. In this work, we consider simultaneously robust and multi-objective design optimization. While the former allows one to deal with uncertainties affecting the objective function, the latter allows for handling multiple conflicting objectives. Conservative quantiles ...Other Conference Item