Open AccessUniversality classes and machine learning
Author(s) -
Vladislav Chertenkov,
Lev N. Shchur
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1740/1/012003
Subject(s) - universality (dynamical systems) , renormalization group , potts model , statistical physics , spins , monte carlo method , computer science , theoretical computer science , theoretical physics , mathematics , artificial intelligence , ising model , physics , mathematical physics , quantum mechanics , statistics , condensed matter physics
We formulate the problem of the universality class investigation using machine learning. We chose an example of the universality class of the two-dimensional 4-state Potts model. There are four known models within the universality class – the 4-state Potts model, the Baxter-Wu model, the Ashkin-Teller model, and the Turban model. All four of them together are not equivalent in the Hamiltonian representation, in the lattice symmetry, and the layout of spins on the lattice. We generate statistically independent datasets for all models using the same Monte Carlo technique. The machine learning methods will be used for the analysis of the universality class of models based on generated datasets.