activity
20172021
most citedDeepMoD: Deep learning for Model Discovery in noisy data

132 citations · 169 across the 8 of their papers we have counts for

collaborators

8 papers

stat.ML2021

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…

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★ 1 cited

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…

cs.LG2020★ 7 cited

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…

physics.comp-ph2019★ 10 cited

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…

physics.comp-ph2019★ 132 cited

DeepMoD: Deep learning for Model Discovery in noisy data

Gert-Jan Both, Subham Choudhury, Pierre Sens +1

We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regres…

cond-mat.soft2017★ 18 cited

Impact of Interaction Range and Curvature on Crystal Growth of Particles Confined to Spherical Surfaces

Stefan Paquay, Gert-Jan Both, Paul van der Schoot

When colloidal particles form a crystal phase on a spherical template, their packing is governed by the effective interaction between them and the elastic strain of bending the gro…