{"title":"拓扑数据分析和机器学习","authors":"D. Leykam, D. Angelakis","doi":"10.1080/23746149.2023.2202331","DOIUrl":null,"url":null,"abstract":"ABSTRACT Topological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research. Graphical abstract","PeriodicalId":7374,"journal":{"name":"Advances in Physics: X","volume":"8 1","pages":""},"PeriodicalIF":7.7000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Topological data analysis and machine learning\",\"authors\":\"D. Leykam, D. Angelakis\",\"doi\":\"10.1080/23746149.2023.2202331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Topological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research. Graphical abstract\",\"PeriodicalId\":7374,\"journal\":{\"name\":\"Advances in Physics: X\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Physics: X\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1080/23746149.2023.2202331\",\"RegionNum\":2,\"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":"Advances in Physics: X","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1080/23746149.2023.2202331","RegionNum":2,"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}
ABSTRACT Topological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research. Graphical abstract
期刊介绍：
Advances in Physics: X is a fully open-access journal that promotes the centrality of physics and physical measurement to modern science and technology. Advances in Physics: X aims to demonstrate the interconnectivity of physics, meaning the intellectual relationships that exist between one branch of physics and another, as well as the influence of physics across (hence the “X”) traditional boundaries into other disciplines including:
Chemistry
Materials Science
Engineering
Biology
Medicine
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