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Surrogate modelling using sparse polynomial chaos expansions: a machine learning flavour
(2024)Computational models have become an integral component across various domains of applied sciences and engineering. These models, often referred to as simulators, play a crucial role in predicting the behavior of complex natural or man-made systems. They empower engineers and scientists to assess a system's performance within a virtual environment, subsequently aiding in the optimization of its design and operation. Simulators, including ...Other Conference Item -
Seismic fragility analysis using mNARX modelling
(2024)Assessing the seismic vulnerability of civil structures is crucial for safeguarding human lives and ensuring the long-term functionality of essential infrastructure. Nevertheless, quantifying the capability of a structure to withstand a potentially large spectrum of seismic events still poses a significant challenge, due to the high level of uncertainty involved. In the current state-of-the art, the uncertainty in the occurrence and ...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 -
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 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
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