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A Study of the Scaling Behavior of the Two-dimensional Ising Model by Methods of Machine Learning Full article

Journal Журнал Сибирского федерального университета. Серия: Математика и физика
ISSN: 1997-1397
Output data Year: 2024, Volume: 17, Number: 2, Pages: 238-245 Pages count : 8
Tags machine learning, convolutional neural networks, Monte Carlo methods, Ising model, scaling, correlation length, magnetic susceptibility
Authors Chubarova A.A. 1 , Mamonova M.V. 1 , Prudnikov P.V. 2
Affiliations
1 Dostoevsky Omsk State University, Omsk, Russian Federation
2 Center of New Chemical Technologies BIC Boreskov Institute of Catalysis SB RAS, Omsk, Russian Federation

Funding (2)

1 Ministry of Science and Higher Education of the Russian Federation FWUR-2024-0039
2 Russian Science Foundation 23-22-00093 (123021200011-7)

Abstract: 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.
Cite: 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. publication_identifier_short.sciact_ihcp_identifier_type
Dates:
Submitted: Sep 10, 2023
Accepted: Jan 27, 2024
Published print: Apr 1, 2024
Identifiers:
publication_identifier.sciact_ihcp_identifier_type: 4372
Citing: Пока нет цитирований