Princeton team's 62-page review unifies control theory, thermodynamics, and machine learning via free energy
ben_eysenbach · x · 2026-11-04
A new arXiv review by Emmy Blumenthal, Benjamin Eysenbach, Gautam Reddy and four others (5 chapters, 62 pages, 16 figures) argues that control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning share one conceptual thread: optimizing free-energy-like functionals under dynamical or statistical constraints.
- Maps bridges including classical mechanics → stochastic control, Schrödinger bridge, Onsager's principle, Jarzynski identity, and the Feynman-Kac formula
- Covers applications in reinforcement learning, variational inference, generative modeling, sampling, and Wasserstein gradient flows
- Written as a self-contained guided tour requiring no prior familiarity, starting from physics principles
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