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Found 6 papers, 6 papers with code
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates
Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics.
Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
We show that language can be a compact, interpretable, and accurate modality for generating physics simulations, paving the way for more usable and accessible PDE solvers.
Strategies for Pretraining Neural Operators
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance.
CaFA: Global Weather Forecasting with Factorized Attention on Sphere
Accurate weather forecasting is crucial in various sectors, impacting decision-making processes and societal events.
Masked Autoencoders are PDE Learners
Furthermore, conditioning neural solvers on learned latent representations can improve time-stepping and super-resolution performance across a variety of coefficients, discretizations, or boundary conditions, as well as on certain unseen PDEs.
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
This introduces a new paradigm where models can be pretrained on unlabeled data from different bearings, faults, and machinery and quickly deployed to new, data-scarce applications to suit specific manufacturing needs.
