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 assistance of experiments, 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，涵盖自然语言处理、图像识别、博弈制胜乃至物理问题求解。尽管将机器学习应用于数值计算与实验辅助的研究已非常丰富，但应用机器学习发现解析方法的研究仍十分匮乏。本文提出了利用Alpha Zero算法进行符号回归以发展物理解析方法的框架，即面向物理的Alpha Zero（AZfP）。作为示范，我们展示了AZfP能够推导Floquet系统中的高频展开形式。AZfP或将为物理理论框架的构建开辟新路径。