DC pole | Wartość | Język |
dc.contributor.author | Richter-Laskowska, Monika | - |
dc.contributor.author | Khan, H. | - |
dc.contributor.author | Trivedi, N. | - |
dc.contributor.author | Maśka, Maciej M. | - |
dc.date.accessioned | 2018-10-08T08:28:28Z | - |
dc.date.available | 2018-10-08T08:28:28Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Condensed Matter Physics, Vol. 21, No 3 (2018), Art. No. 33602 | pl_PL |
dc.identifier.issn | 1607-324X | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/6500 | - |
dc.description.abstract | The Berezinskii-Kosterlitz-Thouless transition is a very speci1c phase transition where all thermodynamic quantities
are smooth. Therefore, it is diWcult 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 identi1cation 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. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | phase transitions | pl_PL |
dc.subject | topological defects | pl_PL |
dc.subject | XY model | pl_PL |
dc.subject | arti1cial neural networks | pl_PL |
dc.subject | machine learning | pl_PL |
dc.title | A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.5488/CMP.21.33602 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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