A Study of the Scaling Behavior of the Two-dimensional Ising Model by Methods of Machine Learning | Sciact - CRIS-система Института катализа

A Study of the Scaling Behavior of the Two-dimensional Ising Model by Methods of Machine Learning Научная публикация

Журнал

Журнал Сибирского федерального университета. Серия: Математика и физика
ISSN: 1997-1397

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

Chubarova A.A. ¹ , Mamonova M.V. ¹ , Prudnikov P.V. ²

Организации

1	Dostoevsky Omsk State University, Omsk, Russian Federation
2	Center of New Chemical Technologies BIC Boreskov Institute of Catalysis SB RAS, Omsk, Russian Federation

Информация о финансировании (2)

1	Министерство науки и высшего образования Российской Федерации (с 15 мая 2018)	FWUR-2024-0039
2	Российский научный фонд	23-22-00093 (123021200011-7)

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.

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
Журнал Сибирского федерального университета. Серия: Математика и физика. 2024. V.17. N2. P.238-245. ЦНХТ ID

Поступила в редакцию:	10 сент. 2023 г.
Принята к публикации:	27 янв. 2024 г.
Опубликована в печати:	1 апр. 2024 г.

ЦНХТ ID:

4372

Пока нет цитирований