23 citations · 40 across the 10 of their papers we have counts for
6 papers · 1 filter
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates
Anthony Zhou, Amir Barati Farimani
Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics. With a few exceptions, neural surrogates general…
Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
Anthony Zhou, Zijie Li, Michael Schneier +2
Recent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much f…
Strategies for Pretraining Neural Operators
Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian +1
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Des…
CaFA: Global Weather Forecasting with Factorized Attention on Sphere
Zijie Li, Anthony Zhou, Saurabh Patil +1
Accurate weather forecasting is crucial in various sectors, impacting decision-making processes and societal events. Data-driven approaches based on machine learning models have re…
Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX
Anthony Zhou, Linnia Hawkins, Pierre Gentine
Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fo…
Masked Autoencoders are PDE Learners
Anthony Zhou, Amir Barati Farimani
Neural solvers for partial differential equations (PDEs) have great potential to generate fast and accurate physics solutions, yet their practicality is currently limited by their…