Search
Results
-
A Unified Benchmarking Platform for UQ Algorithms in UQLab
(2024)Thorough validation and benchmarking against the state-of-the-art are critical components in the development of novel algorithms and tools. Nevertheless, the comprehensive performance comparison between different solutions to the same problem is still sparse in the literature, mostly relegated to dedicated review studies rather than a standard practice. This is especially noticeable in the field of uncertainty quantification, where algorithm ...Other Conference Item -
An introduction to surrogate modelling for uncertainty quantification in computational sciences
(2023)Computational models are nowadays 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 -
Reliability analysis of wind turbines using manifold-NARX surrogate models
(2023)Modeling the dynamic response of systems is essential for structural health monitoring, reliability analysis or design optimization. Structures such as wind turbines, tall buildings or long bridges are submitted to transient excitation such as ground motions or wave and wind loads, which inherently showcase aleatory uncertainties, however. To address uncertainty quantification questions such as sensitivity or reliability analyses, many ...Other Conference Item -
Active learning methods for structural reliability analysis and optimal design
(2023)Other Conference Item -
mNARX - A novel surrogate model for the uncertainty quantification of dynamical systems
(2023)Modelling the dynamic response of civil structures is vital for many applications, including structural health monitoring, reliability analysis and design optimization. These systems often feature responses governed by highly uncertain exogenous excitations, for example, ground motions, wind, or wave loads. To quantify the effects of this uncertainty, many evaluations of the underlying numerical models are usually required. Therefore, ...Other Conference Item -
UQLab & UQ[py]Lab - project updates and outlook
(2023)The adoption of advanced uncertainty quantification techniques is steadily increasing throughout the academic and industrial applications landscape. The assessment of uncertainty in technological systems of societal relevance (e.g. in civil and aerospace engineering) is being gradually mandated, or at least included in engineering construction codes all around the world. In parallel, the design and maintenance of renewable energy systems ...Other Conference Item -
Constructing confidence and prediction intervals for multifidelity surrogate models involving noisy data
(2023)Nowadays, computer simulations, or white-box models, are indispensable to model complex engineering systems that need to be reliable and safe. White-box models can provide accurate predictions when there is a precise underlying physical model, but they may be hard to obtain for highly complex engineering systems, and they often fail to capture reality in its entirety. Sometimes, experimental data are available for the same system. Such ...Other Conference Item -
A data-driven surrogate model for uncertainty quantification of dynamical systems
(2023)Surrogate models have become a standard tool in uncertainty quantification to emulate the response of real-world systems at a low computational cost. Within the HIPERWIND EU project, we recently proposed a class of surrogate models that can accurately model the time-dependent response of dynamical state- and control system-dependent structures excited by high-dimensional exogenous inputs, such as wind turbines (Dimitrov et al. (2022)). ...Other Conference Item -
Surrogate modelling for stochastic simulators
(2023)Computational models, a.k.a. simulators, are used in all fields of engineering and applied sciences to help design and assess complex systems. Advanced analyses such as optimization or uncertainty quantification, which require repeated runs by varying input parameters, cannot be carried out with brute force methods such as Monte Carlo simulation due to computational costs. Thus the recent development of surrogate models such as polynomial ...Other Conference Item -
Defining what is a probability of failure for systems modelled by stochastic simulators
(2023)Reliability analysis is a field of uncertainty quantification primarily concerned with estimating the probability that a system response exceeds a critical threshold, resulting in failure. By design, such a probability of failure is small. Consequently, accurately computing it requires many evaluations of the so-called limit state function, a computational model that classifies whether the system fails or not, and that is often expensive ...Other Conference Item