15 citations · 16 across the 4 of their papers we have counts for
4 papers
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…
Weight Conditioning for Smooth Optimization of Neural Networks
Hemanth Saratchandran, Thomas X. Wang, Simon Lucey
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning. This approach aims to narrow the gap between th…
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…
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…