从数据中学习物理：热力学解释
Learning Physics from Data: a Thermodynamic Interpretation
摘要：
实验数据库通常是非常大和高维的。要从中学习，需要识别重要的特征（模式），这些特征通常以与记录数据不同的尺度呈现。根据在统计力学和热力学中收集的经验，识别模式的过程（学习过程）可以被看作是由熵驱动的耗散时间演化，从详细的描述到不太详细的描述。这就是热力学进入机器学习的方式。另一方面，可逆的（通常是哈密顿的）演化是在描述的层次内传播的，这也是要认识到的。这就是泊松几何进入机器学习的方式。学习处理自由表面液体和阻尼刚体旋转作为一个例子。
英文摘要：
Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience collected in statistical mechanics and thermodynamics, the process of recognizing the pattern (the learning process) can be seen as a dissipative time evolution driven by entropy from a detailed level of description to less detailed. This is the way thermodynamics enters machine learning. On the other hand, reversible (typically Hamiltonian) evolution is propagation within the levels of description, that is also to be recognized. This is how Poisson geometry enters machine learning. Learning to handle free surface liquids and damped rigid body rotation serves as an illustration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
