15 citations · 15 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Operator Learning with Neural Fields: Tackling PDEs on General Geometries
Louis Serrano, Lise Le Boudec, Armand Kassaï Koupaï +4
Machine learning approaches for solving partial differential equations require learning mappings between function spaces. While convolutional or graph neural networks are constrain…