no code implementations • ICLR 2022 • Jérémie Dona, Marie Déchelle, Patrick Gallinari, Marina Levy
A common practice to identify the respective parameters of the physical and ML components is to formulate the problem as supervised learning on observed trajectories.
no code implementations • 21 Jul 2021 • Jérémie Dona, Patrick Gallinari
The second one corresponds to the elimination of redundant information by selecting variables in a correlated fashion which requires modeling the covariance of the binary distribution.
2 code implementations • ICLR 2021 • Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari
In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.
