Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates

arXiv:2412.13074 · cs.LG · Submitted 2024-12-17 · Read on arXiv

cs.LG

Submitted: 2024-12-17

Updated: 2025-04-16

Code: https://github.com/anthonyzhou-1/temporal

Terminology

• Neural Surrogates — 4× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode

Sources

• Fourier Neural Operator for Parametric Partial Differential Equations
• Neural Operator: Learning Maps Between Function Spaces
• Choose a Transformer: Fourier or Galerkin
• Interaction Networks for Learning about Objects, Relations and Physics
• Transformer for Partial Differential Equations' Operator Learning
• Towards Multi-spatiotemporal-scale Generalized PDE Modeling
• Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
• Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
• Scalable Transformer for PDE Surrogate Modeling
• Transolver: A Fast Transformer Solver for PDEs on General Geometries
• PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs
• Poseidon: Efficient Foundation Models for PDEs
• Strategies for Pretraining Neural Operators
• U-NO: U-shaped Neural Operators
• CaFA: Global Weather Forecasting with Factorized Attention on Sphere
• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Learning Dissipative Dynamics in Chaotic Systems
• Lie Point Symmetry Data Augmentation for Neural PDE Solvers
• GraphCast: Learning skillful medium-range global weather forecasting

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