Machine learning with neural networks is now becoming a more and more powerful tool for various tasks, such as natural language processing, image recognition, winning the game, and even for the issues of physics. Although there are many studies on the application of machine learning to numerical calculation and the assistance of experimental detection, the methods of applying machine learning to find the analytical method are poorly studied. In this paper, we propose the frameworks of developing analytical methods in physics by using the symbolic regression with the Alpha Zero algorithm, that is Alpha Zero for physics (AZfP). As a demonstration, we show that AZfP can derive the high-frequency expansion in the Floquet systems. AZfP may have the possibility of developing a new theoretical framework in physics.

翻译：基于神经网络的机器学习正日益成为各类任务的强大工具，如自然语言处理、图像识别、博弈获胜，甚至包括物理学问题。尽管已有大量研究将机器学习应用于数值计算和实验检测辅助，但利用机器学习发现分析方法的研究仍相对匮乏。本文提出了一种基于Alpha Zero算法结合符号回归的物理学分析方法开发框架，即面向物理学的Alpha Zero（AZfP）。作为示例，我们展示了AZfP能够推导出Floquet系统中的高频展开。AZfP有望为物理学新理论框架的开发提供可能。