Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental connections between subdisciplines of physics, applied mathematics, and machine learning. In this review, we bring together some of these ideas, often expressed in different languages, to highlight a conceptual thread that links five distinct fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. A common theme is the optimization of free-energy-like functionals under dynamical or statistical constraints. We offer a guided tour through this thread ...
| Authors | Emmy Blumenthal, Nikolas Claussen, Benjamin Eysenbach et al. |
|---|---|
| Primary category | cond-mat.stat-mech |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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