Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Topological data analysis and machine learning

Leykam Daniel, Angelakis Dimitrios

Simple record

URIhttp://purl.tuc.gr/dl/dias/0CBDC945-F21A-4865-8B9D-21638F9F146A-
Identifierhttps://doi.org/10.1080/23746149.2023.2202331-
Identifierhttps://www.tandfonline.com/doi/full/10.1080/23746149.2023.2202331-
Languageen-
Extent24 pagesen
TitleTopological data analysis and machine learningen
CreatorLeykam Danielen
CreatorAngelakis Dimitriosen
CreatorΑγγελακης Δημητριοςel
PublisherTaylor & Francisen
DescriptionThis research is supported by the EU HORIZON—Project 101080085 — QCFD.en
Content SummaryTopological 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.en
Type of ItemΑνασκόπησηel
Type of ItemReviewen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2025-05-29-
Date of Publication2023-
SubjectMachine learningen
SubjectStrongly correlated quantum systemsen
SubjectPersistent homologyen
SubjectPhase transitionen
SubjectQuantum computingen
SubjectCondensed matter physicsen
SubjectTopological phaseen
Bibliographic CitationD. Leykam and D. G. Angelakis, “Topological data analysis and machine learning,” Adv. Phys.: X, vol. 8, no. 1, Dec. 2023, doi: 10.1080/23746149.2023.2202331.en

Available Files

Services

Statistics

 

Copyright © DIAS 2013