132 citations · 231 across the 14 of their papers we have counts for
13 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…
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,…
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…
Sparsely constrained neural networks for model discovery of PDEs
Gert-Jan Both, Gijs Vermarien, Remy Kusters
Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set. These fe…
Temporal Normalizing Flows
Gert-Jan Both, Remy Kusters
Analyzing and interpreting time-dependent stochastic data requires accurate and robust density estimation. In this paper we extend the concept of normalizing flows to so-called tem…