3 papers
cs.LG2025
Efficient Generative Transformer Operators For Million-Point PDEs
Armand Kassaï Koupaï, Lise Le Boudec, Patrick Gallinari
We introduce ECHO, a transformer-operator framework for generating million-point PDE trajectories. While existing neural operators (NOs) have shown promise for solving partial diff…
cs.LG2025
ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Armand Kassaï Koupaï, Lise Le Boudec, Louis Serrano +1
Solving time-dependent parametric partial differential equations (PDEs) remains a fundamental challenge for neural solvers, particularly when generalizing across a wide range of ph…
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…