244 citations · 1.3k across the 42 of their papers we have counts for
6 papers · 1 filter
Analyzing Political Text at Scale with Online Tensor LDA
Sara Kangaslahti, Danny Ebanks, Jean Kossaifi +3
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Di…
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh +7
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed…
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi +4
A wide range of scientific problems, such as those described by continuous-time dynamical systems and partial differential equations (PDEs), are naturally formulated on function sp…
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Ryan Y. Lin, Julius Berner, Valentin Duruisseaux +5
Physics-informed neural operators offer a powerful framework for learning solution operators of partial differential equations (PDEs) by combining data and physics losses. However,…
Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction
Chris Choy, Alexey Kamenev, Jean Kossaifi +3
Computational Fluid Dynamics (CFD) is crucial for automotive design, requiring the analysis of large 3D point clouds to study how vehicle geometry affects pressure fields and drag…
TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training
Sebastian Loeschcke, David Pitt, Robert Joseph George +5
Scientific problems require resolving multi-scale phenomena across different resolutions and learning solution operators in infinite-dimensional function spaces. Neural operators p…