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. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| Zusammenfassung: | 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 particular, we study how the accuracy
of the transition identification depends on the way the neural networks are trained. We apply our approach to
three different systems: (i) the classical XY model, (ii) the phase-fermion model, where classical and quantum
degrees of freedom are coupled and (iii) the quantum XY model. |
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