One of the most exciting applications of AI is automated scientific discovery based on previously amassed data, coupled with restrictions provided by the known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Of particular importance are complex dynamic systems where their time evolution is strongly influenced by varying external parameters. In this paper we develop a platform based on a generalised Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. We focus on systems whose complexity and sheer sizes render complete microscopic description impractical, and constructing theoretical macroscopic models requires extensive domain knowledge or trial-and-error. Our machine learning approach addresses this by simultaneously constructing reduced thermodynamic coordinates and interpreting the dynamics on these coordinates. We demonstrate our method by studying theoretically and validating experimentally, the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including (1) the identification of stable and transition states and (2) the control of the stretching rate. We further demonstrate the universality of our approach by applying it to an unrelated problem in a different domain: constructing macroscopic dynamics for spatial epidemics, showing that our method addresses wide scientific and technological applications.

翻译：人工智能最令人兴奋的应用之一是基于先前积累的数据，结合已知物理原理（包括对称性和守恒定律）的限制，实现自动化的科学发现。这种自动化的假设创建与验证能够帮助科学家研究传统物理直觉可能失效的复杂现象。特别重要的是那些时间演化受变化的外部参数强烈影响的复杂动态系统。本文基于广义昂萨格原理开发了一个平台，能够直接从微观轨迹的观测中学习任意随机耗散系统的宏观动力学描述。我们关注的是那些由于复杂性和规模巨大而使得完整微观描述不切实际，且构建理论宏观模型需要大量领域知识或反复试错的系统。我们的机器学习方法通过同时构建简化的热力学坐标并解释这些坐标上的动力学来应对这一挑战。我们通过理论研究和实验验证了在外加场中长聚合物链的拉伸过程。具体而言，我们学习了三个可解释的热力学坐标，并构建了聚合物拉伸的动态景观，包括：(1) 识别稳定态和过渡态，(2) 控制拉伸速率。我们进一步将这一方法应用于不同领域的无关问题——构建空间流行病的宏观动力学，展示了其普适性，表明我们的方法能够满足广泛的科学和技术应用需求。