Harnessing data using symbolic regression methods for discovering novel paradigms in physics  

作　　者：Jianyang Guo Wan-Jian Yin 

机构地区：[1]College of Energy,Soochow Institute for Energy and Materials Innovations(SIEMIS),Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,Soochow University,Suzhou 215006,China [2]Shanghai Qi Zhi Institute,Shanghai 200232,China [3]Light Industry Institute of Electrochemical Power Sources,Soochow University,Suzhou 215006,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2024年第6期1-11,共11页中国科学：物理学、力学、天文学（英文版）

基　　金：support of the College of Energy,Soochow Institute for Energy and Materials Innovations(SIEMIS);Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies of Soochow University;Shanghai Qi Zhi Institute;Light Industry Institute of Electrochemical Power Sources of Soochow University。

摘　　要：In recent years,machine-learning methods have profoundly impacted research in the interdisciplinary fields of physics.However,most machine-learning models lack interpretability,and physicists doubt the credibility of their conclusions because they cannot be combined with prior physical knowledge.Therefore,this review focuses on symbolic regression,which is an interpretable machine-learning method.First,the relevant concepts of machine learning are introduced in conjunction with induction.Next,we provide an overview of symbolic regression methods.Subsequently,the recent directions for the application of symbolic regression methods in different subfields of physics are outlined,and an overview of the ways in which the applications of symbolic regression have evolved in the realm of physics is provided.The major aim of this review is to introduce the basic principles of symbolic regression and explain its applications in the field of physics.

关 键 词：machine learning genetic programming symbolic regression 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O4[自动化与计算机技术—控制科学与工程]