Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session EE01: V: Statistical and Nonlinear Physics II
11:30 AM–1:18 PM,
Tuesday, March 5, 2024
Room: Virtual Room 01
Sponsoring
Unit:
GSNP
Chair: Raffaele Marino, Università degli studi di Firenze
Abstract: EE01.00004 : Phase classification and finite-size analysis with supervised machine learning*
12:06 PM–12:18 PM
Presenter:
Lev Shchur
(Landau ITP - Chernogolovka)
Author:
Lev Shchur
(Landau ITP - Chernogolovka)
We used this approach to investigate how the anisotropy of the model being tested affects the accuracy of the critical temperature and critical index estimates by conducting a training and validation test run on an isotropic model. In other words, we would like to understand how stable the predictions are on a test set with unknown isotropic properties. We simulate a two-dimensional Ising model with two types of anisotropy using precise knowledge of the orthogonal anisotropy of the Onsager solution and the Huthappel solution of the diagonal anisotropy. The result is interesting in that the universal property of the Ising model, expressed in the correlation length exponent, is well restored as a result of supervised machine learning, while the non-universal property - the critical temperature value - is shifted in some regular way. This is true even for the antiferromagnetic sector of the Hautapell model.
[1] V. Chertenkov, E. Burovski, and L. Shchur, PRE 108, L032102 (2023)
*Research supported by the grant 22-11-00259 of the Russian Science Foundation.The simulations were done using the computational resources of HPC facilities at HSE University.
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