- Issue
- Journal of Siberian Federal University. Mathematics & Physics. 2024 17 (2)
- Authors
- Chubarova, Alina A.; Mamonova, Marina V.; Prudnikov, Pavel V.
- Contact information
- Chubarova, Alina A.: Dostoevsky Omsk State University Omsk, Russian Federation; OCRID: 0009-0009-0414-1963; Mamonova, Marina V. : Dostoevsky Omsk State University Omsk, Russian Federation; OCRID: 0000-0001-7466-086X; Prudnikov, Pavel V.: Center of New Chemical Technologies BIC Boreskov Institute of Catalysis SB RAS Omsk, Russian Federation; OCRID: 0000-0002-6522-2873
- Keywords
- machine learning; convolutional neural networks; Monte Carlo methods; Ising model; scaling; correlation length; magnetic susceptibility
- Abstract
In the field of condensed matter physics, machine learning methods have become an increas- ingly 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 dimen- sions. 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
- Pages
- 238–245
- EDN
- MDLPVA
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/152676
Journal of Siberian Federal University. Mathematics & Physics / A Study of the Scaling Behavior of the Two-dimensional Ising Model by Methods of Machine Learning
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