activity
20202023
most citedAugmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

108 citations · 123 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023★ 3 cited

Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces

Raphael Boige, Guillaume Richard, Jérémie Dona +2

Quality Diversity (QD) algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. While e…

cs.LG2022★ 1 cited

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà +3

Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different…

cs.LG2021

Differentiable Feature Selection, a Reparameterization Approach

Jérémie Dona, Patrick Gallinari

We consider the task of feature selection for reconstruction which consists in choosing a small subset of features from which whole data instances can be reconstructed. This is of…

stat.ML2020★ 108 cited

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Yuan Yin, Vincent Le Guen, Jérémie Dona +4

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purel…

cs.LG2020★ 11 cited

PDE-Driven Spatiotemporal Disentanglement

Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier +1

A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differen…