Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency
cs.LG, cs.AI
Submitted: 2026-05-08
Updated: 2026-09-26
Terminology
Sources
- CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
- Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems
- Neural Operator: Graph Kernel Network for Partial Differential Equations
- Generative Latent Neural PDE Solver using Flow Matching
- Learning to Integrate Diffusion ODEs by Averaging the Derivatives
- Learning Diffeomorphism for Image Registration with Time-Continuous Networks using Semigroup Regularization
- Lie Flow: Video Dynamic Fields Modeling and Predicting with Lie Algebra as Geometric Physics Principle
- FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling
- GeoWorld: Geometric World Models
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