10 citations · 11 across the 3 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
stat.ML2021★ 1 cited
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…
stat.ML2021
Fully differentiable model discovery
Gert-Jan Both, Remy Kusters
Model discovery aims at autonomously discovering differential equations underlying a dataset. Approaches based on Physics Informed Neural Networks (PINNs) have shown great promise,…
physics.comp-ph2021
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…