3 papers
cs.LG2026
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
Lise Le Boudec, Emmanuel de Bezenac, Louis Serrano +3
Physics-informed deep learning often faces optimization challenges due to the complexity of solving partial differential equations (PDEs), which involve exploring large solution sp…
cs.LG2024
GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
Armand Kassaï Koupaï, Jorge Mifsut Benet, Yuan Yin +2
Solving parametric partial differential equations (PDEs) presents significant challenges for data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in…
cs.LG2024
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Etienne Le Naour, Louis Serrano, Léon Migus +5
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular sample…