15 citations · 18 across the 9 of their papers we have counts for
10 papers
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…
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…
JAFAR: Jack up Any Feature at Any Resolution
Paul Couairon, Loick Chambon, Louis Serrano +3
Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to pro…
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…
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…
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…
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
Louis Serrano, Thomas X Wang, Etienne Le Naour +2
We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Ou…
INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations
Louis Serrano, Leon Migus, Yuan Yin +2
For numerical design, the development of efficient and accurate surrogate models is paramount. They allow us to approximate complex physical phenomena, thereby reducing the computa…
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…