Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
Review connects control theory, optimal transport, probabilistic inference, thermodynamics, and machine learning via free-energy optimization under constraints.
The review unifies five fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. The common conceptual thread is optimization of free-energy-like functionals under dynamical or statistical constraints. Selected applications are presented in reinforcement learning, variational inference, and generative modeling. The tutorial-style text assumes no prior familiarity and begins from physics principles.
- Links control, optimal transport, inference, thermodynamics, and ML in one framework
- Common theme: free-energy-like functional optimization under constraints
- Applications span reinforcement learning, variational inference, generative modeling
- Accessible introduction starting from physics principles
From the original article
1 min read at arxiv.orgThe 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.
By Emmy Blumenthal · arxiv.org
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