research
∙
07/09/2023
Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks
Monte Carlo Markov Chain (MCMC) methods commonly confront two fundamenta...
share
research
∙
06/28/2023
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)
In this study, we propose a novel surrogate modelling approach to effici...
research
∙
05/31/2023
Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis
System reliability analysis aims at computing the probability of failure...
research
∙
07/12/2022
A spectral surrogate model for stochastic simulators computed from trajectory samples
Stochastic simulators are non-deterministic computer models which provid...
research
∙
07/01/2021
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions
Variance-based global sensitivity analysis, in particular Sobol' analysi...
research
∙
09/10/2020
Sparse Polynomial Chaos Expansions: Solvers, Basis Adaptivity and Meta-selection
Sparse polynomial chaos expansions (PCEs) are an efficient and widely us...
research
∙
02/04/2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
Sparse polynomial chaos expansions are a popular surrogate modelling met...
research
∙
09/05/2017