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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/02683BED-6337-4F55-BDC1-8E70C273E69B | - |
| Identifier | https://doi.org/10.1109/CLEO/Europe-EQEC52157.2021.9541650 | - |
| Identifier | https://ieeexplore.ieee.org/document/9541650 | - |
| Language | en | - |
| Extent | 1 page | en |
| Title | Characterizing photonic band structures using topological data analysis | en |
| Creator | Leykam Daniel | en |
| Creator | Angelakis Dimitrios | en |
| Creator | Αγγελακης Δημητριος | el |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | Topological 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 Item | Σύντομη Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Short Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-05-24 | - |
| Date of Publication | 2021 | - |
| Subject | Data analysis | en |
| Subject | Shape | en |
| Subject | Europe | en |
| Subject | Machine learning | en |
| Subject | Photonics | en |
| Bibliographic Citation | D. 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 |
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