{"title":"使用符号回归方法利用数据发现物理学新范式","authors":"Jianyang Guo, Wan-Jian Yin","doi":"10.1007/s11433-023-2346-2","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":774,"journal":{"name":"Science China Physics, Mechanics & Astronomy","volume":null,"pages":null},"PeriodicalIF":6.4000,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing data using symbolic regression methods for discovering novel paradigms in physics\",\"authors\":\"Jianyang Guo, Wan-Jian Yin\",\"doi\":\"10.1007/s11433-023-2346-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":774,\"journal\":{\"name\":\"Science China Physics, Mechanics & Astronomy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2024-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science China Physics, Mechanics & Astronomy\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11433-023-2346-2\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science China Physics, Mechanics & Astronomy","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s11433-023-2346-2","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Harnessing data using symbolic regression methods for discovering novel paradigms in physics
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.
期刊介绍:
Science China Physics, Mechanics & Astronomy, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research.
Science China Physics, Mechanics & Astronomy, is published in both print and electronic forms. It is indexed by Science Citation Index.
Categories of articles:
Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested.
Research papers report on important original results in all areas of physics, mechanics and astronomy.
Brief reports present short reports in a timely manner of the latest important results.