arXiv (All)
open access
Discovering Hidden Predictive States in Kinetic Transport
How do autonomous macroscopic transport laws emerge from kinetic dynamics, and which hidden degrees of freedom must survive coarse-graining? We address these questions by formulating kinetic state reduction as a problem of predictive sufficiency rather than conventional moment truncation. Starting f
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
- Physics-Transfer Learning: A Physics-Constrained Theory for Zero-Shot Generalization Along the Ladder of Complexity#1386355
- Kinetic Optimal Transport (OTIKIN) -- Part 1: Second-order discrepancies between probability measures#264896
- Unified understanding of mesoscale and macroscale transport phenomena from a microscopic particle description#1134298
- Unified understanding of mesoscale and macroscale transport phenomena from a microscopic particle description#1134276
- Speed up of passive tracers in mixtures with active chemical reactions#1077345
Record · ID 1412349
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