A study of the scaling behavior of the two-dimensional Ising model by methods of machine learning Научная публикация
| Журнал |
Journal of Siberian Federal University. Mathematics & Physics
ISSN: 1997-1397 , E-ISSN: 2313-6022 |
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| Вых. Данные | Год: 2024, Том: 17, Номер: 2, Страницы: 238-245 Страниц : 8 | ||||
| Ключевые слова | machine learning, convolutional neural networks, Monte Carlo methods, Ising model, scaling, correlation length, magnetic susceptibility | ||||
| Авторы |
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| Организации |
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Информация о финансировании (2)
| 1 | Российский научный фонд | 23-22-00093 |
| 2 | Министерство науки и высшего образования Российской Федерации | FWUR-2024-0039 |
Реферат:
In the field of condensed matter physics, machine learning methods have become an increasingly important instrument for researching phase transitions. Here we present a method for calculating the universal characteristics of spin models using an Ising model that is exactly solvable in two dimensions. The method is based on a convolutional neural network (CNN) with controlled learning. The scaling functions prove the continuing type of phase transition for the 2D Ising model. As a result of the proposed technique, it has been possible to calculate correlation length directly
Библиографическая ссылка:
Chubarova A.A.
, Mamonova M.V.
, Prudnikov P.V.
A study of the scaling behavior of the two-dimensional Ising model by methods of machine learning
Journal of Siberian Federal University. Mathematics & Physics. 2024. V.17. N2. P.238-245. WOS РИНЦ
A study of the scaling behavior of the two-dimensional Ising model by methods of machine learning
Journal of Siberian Federal University. Mathematics & Physics. 2024. V.17. N2. P.238-245. WOS РИНЦ
Даты:
| Поступила в редакцию: | 10 сент. 2023 г. |
| Принята к публикации: | 27 янв. 2024 г. |
Идентификаторы БД:
| Web of science: | WOS:001203106200009 |
| РИНЦ: | 66229553 |