Transfer Machine Learning of an Anisotropic Model

• Published in: Lobachevskii journal of mathematics Vol. 46; no. 1; pp. 528 - 534
• Main Authors: Sukhoverkhova, D. D., Shchur, L. N.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.01.2025; Springer Nature B.V
• Subjects: Algebra; Analysis; Anisotropy; Classification; Critical temperature; Dimensional analysis; Ferromagnetism; Geometry; Ising model; Machine learning; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Neural networks; Phase transitions; Probability Theory and Stochastic Processes; Supervised learning; Transfer machines; Transition temperature; supervised machine learning; lsing model; transfer learning; universality class; critical behavior; 2010 Mathematics Subject Classification: 68U20, 68W40
• ISSN: 1995-0802; 1818-9962
• DOI: 10.1134/S1995080224607835

We investigate the possibility of extracting features of second-order phase transitions using transfer machine learning. We have performed supervised machine learning for binary classification of snapshots of the spin distribution of the isotropic Ising model. The binary classification is performed in ferromagnetic and paramagnetic phases using a known critical temperature. The trained network is used to predict whether a snapshot obtained from model simulations with orthogonal anisotropy belongs to the paramagnetic phase. Using finite-dimensional prediction analysis, we estimate the critical temperature and the exponent of the correlation length. This gives us an estimate of the interval of the anisotropy parameter in which the neural network can make correct estimates.