no code implementations • 30 Nov 2022 • M. Moustapha, B. Sudret
A crucial aspect in surrogate modelling is the assumption of smoothness and regularity of the model to approximate.
no code implementations • 3 Mar 2022 • M. Moustapha, A. Galimshina, G. Habert, B. Sudret
The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric.
no code implementations • 9 Jun 2021 • P. -R. Wagner, S. Marelli, I. Papaioannou, D. Straub, B. Sudret
Estimating the probability of rare failure events is an essential step in the reliability assessment of engineering systems.
no code implementations • 3 Jun 2021 • M. Moustapha, S. Marelli, B. Sudret
Using this framework, we devise 39 strategies for the solution of $20$ reliability benchmark problems.
no code implementations • 2 Jul 2020 • X. Zhu, B. Sudret
Stochastic simulators are ubiquitous in many fields of applied sciences and engineering.
Epidemiology Computation Methodology
no code implementations • 4 May 2020 • X. Zhu, B. Sudret
Due to this random nature, conventional Sobol' indices, used in global sensitivity analysis, can be extended to stochastic simulators in different ways.
no code implementations • 9 Apr 2020 • S. Marelli, P. -R. Wagner, C. Lataniotis, B. Sudret
Constructing approximations that can accurately mimic the behavior of complex models at reduced computational costs is an important aspect of uncertainty quantification.
no code implementations • 20 Nov 2019 • X. Zhu, B. Sudret
Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task.
no code implementations • 15 Dec 2018 • C. Lataniotis, S. Marelli, B. Sudret
Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ).
Supervised dimensionality reduction Uncertainty Quantification
no code implementations • 9 Aug 2018 • E. Torre, S. Marelli, P. Embrechts, B. Sudret
We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE).
no code implementations • 13 Feb 2015 • R. Schoebi, B. Sudret, J. Wiart
These two techniques have been developed more or less in parallel so far with little interaction between the researchers in the two fields.