一篇综述论文探讨控制理论、最优传输等五大学科与机器学习的联系
Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
这篇论文适合想了解机器学习与物理学科交叉的读者，它用物理原理作为起点，解释了机器学习中的优化问题。
这篇论文综述了控制理论、最优传输、概率推断、非平衡热力学和机器学习之间的联系。它指出这些领域都涉及在动态或统计约束下优化自由能类似的功能。论文通过强化学习、变分推断和生成模型等应用展示了这一联系。
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 and present selected applications in reinforcement learning, variational inference, and generative modeling. The review does not assume prior familiarity with these topics, and begins with principles originating from physics.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。涉及核心实体与技术议题包括：machine-learning、control-theory、optimal-transport、reinforcement-learning，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Emmy Blumenthal, Nikolas Claussen, Benjamin Eysenbach, Catherine Ji, Gautam Reddy, Colin Scheibner, Benjamin Sorkin），经 AITOP 多维评分管线结构化提炼。