Journal of Siberian Federal University. Mathematics & Physics / A Study of the Scaling Behavior of the Two-dimensional Ising Model by Methods of Machine Learning

Full text (.pdf)
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