Geodesic
A study of the scaling behavior of the two-dimensional Ising model by methods of machine learning
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 17 (2024) no. 2, pp. 238-245

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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.
Keywords: machine learning, convolutional neural networks, Monte Carlo methods, Ising model, scaling, correlation length, magnetic susceptibility. 
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Alina A. Chubarova; Marina V. Mamonova; Pavel V. Prudnikov. A study of the scaling behavior of the two-dimensional Ising model by methods of machine learning. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 17 (2024) no. 2, pp. 238-245. http://geodesic.mathdoc.fr/item/JSFU_2024_17_2_a9/