4 citations · 6 across the 4 of their papers we have counts for
4 papers
Discovering PDEs from Multiple Experiments
Georges Tod, Gert-Jan Both, Remy Kusters
Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experi…
Sparsistent Model Discovery
Georges Tod, Gert-Jan Both, Remy Kusters
Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific field…
Model discovery in the sparse sampling regime
Gert-Jan Both, Georges Tod, Remy Kusters
To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interp…
Inverse Parametric Uncertain Identification using Polynomial Chaos and high-order Moment Matching benchmarked on a Wet Friction Clutch
Wannes De Groote, Tom Lefebvre, Georges Tod +4
A numerically efficient inverse method for parametric model uncertainty identification using maximum likelihood estimation is presented. The goal is to identify a probability model…