arXiv Machine Learning
Blind Thermodynamic Ontology Discovery from Anonymous Experiments
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Discovering Physical Representation Languages
arXiv:2609.23381v1 Announce Type: new Abstract: Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or exte...
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Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
arXiv:2608. 02662v1 Announce Type: cross Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making.
arXiv Machine Learning
Aug 7
SEAM: Global consistency beyond local accuracy in scientific machine learning
arXiv:2608. 05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction.
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When is a System Discoverable from Data? Discovery Requires Chaos
arXiv:2511. 08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences.
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Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems
arXiv:2606. 07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details.
arXiv Machine Learning
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Structure-preserving uncertainty quantification for GENERIC dynamics
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