A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models
The Berezinskii-Kosterlitz-Thouless transition is a very specific phase transition where all thermodynamic quantities are smooth. Therefore, it is difficult to determine the critical temperature in a precise way. In this paper we demonstrate how neural networks can be used to perform this task. In...
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| Datum: | 2018 |
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| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
Інститут фізики конденсованих систем НАН України
2018
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| Schriftenreihe: | Condensed Matter Physics |
| Online Zugang: | http://dspace.nbuv.gov.ua/handle/123456789/157119 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Zitieren: | A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models / M. Richter-Laskowska, H. Khan, N. Trivedi, M.M. Maśka // Condensed Matter Physics. — 2018. — Т. 21, № 3. — С. 33602: 1–11. — Бібліогр.: 32 назв. — англ. |