arXiv (All)
open access
Autonomous Emergence of Hamiltonian in Deep Generative Models
The unprecedented predictive success of deep generative models in complex many-body systems, such as AlphaFold 3, raises an epistemological question: do these networks merely memorize data distributions via high-dimensional interpolation, or do they autonomously deduce the underlying physical laws?
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-Informed Non-Dimensionalisation for Neural Solvers#684012
- The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning#1117790
- The Deep Structure of Lives#746542
- Mathematical Modeling of Physical Reality: From Numbers to Fractals, Quantum Mechanics and the Standard Model#74338
- Climate Physics Dynamic Matching#780764
Record · ID 973139
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