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

Search

Browse

My Space

Characterizing photonic band structures using topological data analysis

Leykam Daniel, Angelakis Dimitrios

Simple record


URIhttp://purl.tuc.gr/dl/dias/02683BED-6337-4F55-BDC1-8E70C273E69B-
Identifierhttps://doi.org/10.1109/CLEO/Europe-EQEC52157.2021.9541650-
Identifierhttps://ieeexplore.ieee.org/document/9541650-
Languageen-
Extent1 pageen
TitleCharacterizing photonic band structures using topological data analysisen
CreatorLeykam Danielen
CreatorAngelakis Dimitriosen
CreatorΑγγελακης Δημητριοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryTopological data analysis forms a suite of techniques for characterizing the abstract "shapes" of complex high-dimensional data. Being sensitive to global features, topological data analysis shows promise for the unsupervised machine learning of order parameters and topological phases. Here we show how the topological data analysis technique of persistent homology may be applied to characterize photonic band structures and learn their topological features.en
Type of ItemΣύντομη Δημοσίευση σε Συνέδριοel
Type of ItemConference Short Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-24-
Date of Publication2021-
SubjectData analysisen
SubjectShapeen
SubjectEuropeen
SubjectMachine learningen
SubjectPhotonicsen
Bibliographic CitationD. Leykam and D. G. Angelakis, "Characterizing photonic band structures using topological data analysis," presented at the 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2021, doi: 10.1109/CLEO/Europe-EQEC52157.2021.9541650.en

Services

Statistics