arXiv (OAI Expanded)
open access
Climate Physics Dynamic Matching
Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as black boxes that neglect underlying physical structure, while physics-based models governed by partial differential equations are often incomplete due t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Climate Physics Dynamic Matching#780764
- Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling#1129199
- Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics#984708
- Differentiating through Stochastic Differential Equations: A Primer#1156255
- SINO: Scale-Invariant Neural Operator#1132091
Record · ID 779389
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive