15 citations · 16 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
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…