Alpha Zero for Physics: Application of Symbolic Regression with Alpha Zero to find the analytical methods in physics

Autor:	Michishita, Yoshihiro
Rok vydání:	2023
Předmět:	Physics - Computational Physics Condensed Matter - Disordered Systems and Neural Networks Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 4 figures and a table in the main text. The source codes are uploaded on Git Hub
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.12713 Zobrazit plný text záznamu View this record from Arxiv