| Operator Learning with Neural Fields: Tackling PDEs on General Geometries L Serrano, LL Boudec, AK Koupaï, TX Wang, Y Yin, JN Vittaut, P Gallinari Neurips 2023, 2023 | 8 | 2023 |
| INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations L Serrano, L Migus, Y Yin, JA Mazari, P Gallinari Workshop SynS and ML, ICML 2023., 2023 | 3 | 2023 |
| Time series continuous modeling for imputation and forecasting with implicit neural representations EL Naour, L Serrano, L Migus, Y Yin, G Agoua, N Baskiotis, V Guigue arXiv preprint arXiv:2306.05880, 2023 | 2 | 2023 |
| OPERATOR LEARNING ON FREE-FORM GEOMETRIES L Serrano, JN Vittaut, P Gallinari ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 2 | 2023 |
| Zebra: a continuous generative transformer for solving parametric PDEs L Serrano, P ERBACHER, JN Vittaut, P Gallinari ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | | 2024 |
| Learning iterative algorithms to solve PDEs. L Le Boudec, E de Bezenac, L Serrano, Y Yin ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | | 2024 |
| Latent Diffusion Transformer with Local Neural Field as PDE Surrogate Model L Serrano, JN Vittaut, P Gallinari ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | | 2024 |
| TIMEFLOW: AN IMPLICIT NEURAL REPRESENTATION APPROACH FOR CONTINUOUS TIME SERIES MODEL E Le Naour, L Serrano, L Migus, Y Yin, G Agoua, N Baskiotis, P Gallinari, ... | | |
