7 papers · 1 filter
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…
Test-time Generalization for Physics through Neural Operator Splitting
Louis Serrano, Jiequn Han, Edouard Oyallon +2
Neural operators have shown promise in learning solution maps of partial differential equations (PDEs), but they often struggle to generalize when test inputs lie outside the train…
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…
AB-UPT for Automotive and Aerospace Applications
Benedikt Alkin, Richard Kurle, Louis Serrano +2
The recently proposed Anchored-Branched Universal Physics Transformers (AB-UPT) shows strong capabilities to replicate automotive computational fluid dynamics simulations requiring…
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
Adel Nabli, Louis Fournier, Pierre Erbacher +3
Training LLMs relies on distributed implementations using multiple GPUs to compute gradients in parallel with sharded optimizers. However, synchronizing gradients in data parallel…
Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
Louis Serrano, Armand Kassaï Koupaï, Thomas X Wang +2
Solving time-dependent parametric partial differential equations (PDEs) is challenging for data-driven methods, as these models must adapt to variations in parameters such as coeff…