| Augmenting physical models with deep networks for complex dynamics forecasting Y Yin, V Le Guen, J Dona, E de Bézenac, I Ayed, N Thome, P Gallinari Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124012, 2021 | 142 | 2021 |
| Pde-driven spatiotemporal disentanglement J Donà, JY Franceschi, S Lamprier, P Gallinari arXiv preprint arXiv:2008.01352, 2020 | 32 | 2020 |
| Generalizing to new physical systems via context-informed dynamics model M Kirchmeyer, Y Yin, J Donà, N Baskiotis, A Rakotomamonjy, P Gallinari International Conference on Machine Learning, 11283-11301, 2022 | 25 | 2022 |
| Differentiable feature selection, a reparameterization approach J Dona, P Gallinari Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 8 | 2021 |
| Constrained physical-statistics models for dynamical system identification and prediction J Donà, M Déchelle, M Levy, P Gallinari ICLR 2022-The Tenth International Conference on Learning Representations, 2022 | 6 | 2022 |
| Bridging dynamical models and deep networks to solve forward and inverse problems M Déchelle, J Donà, K Plessis-Fraissard, P Gallinari, M Lévy NeurIPS 2020-1st NeurIPS workshop on Interpretable Inductive Biases and …, 2021 | 5 | 2021 |
| Gradient-informed quality diversity for the illumination of discrete spaces R Boige, G Richard, J Dona, T Pierrot, A Cully Proceedings of the Genetic and Evolutionary Computation Conference, 119-128, 2023 | 2 | 2023 |
| Statistical learning of physical dynamics J Donà Sorbonne Université, 2022 | | 2022 |
