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arXiv paper links control theory, inference, transport and thermodynamics in learning
A new arXiv preprint surveys how methods for learning structure from high-dimensional data connect to ideas from control theory, statistical inference, optimal transport and thermodynamics. The author argues these shared mathematical foundations bridge physics and applied mathematics with machine learning. The paper also outlines applications of this unified perspective.