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Список литературы
|
|
|
1. |
F.A.Gomez Albarracin, H.D.Rosales, “Machine learning techniques to construct detailed phase diagrams for skyrmion systems”, Phys. Rev. B, 105 (2022), 214423 |
2. |
W.Wang, Z.Wang, Y.Zhang, B.Sun, K.Xia, “Learning Order Parameters from Videos of Skyrmion Dynamical Phases with Neural Networks”, Phys. Rev. V, 16 (2021), 014005 |
3. |
M.Kumar, V.Banerjee, S.Puri, M.Weigel, “Critical behavior of the three-state random-field Potts model in three dimensions”, Phys. Rev. Res., 4 (2022), L042041 |
4. |
C.W.Glass, A.R.Oganov, N.Hansen, “USPEX – evolutionary crystal structure prediction”, Comp. Phys. Comm., 175 (2006), 713–720 |
5. |
C.W.Glass, A.R.Oganov, “Crystal structure prediction using ab initio evolutionary techniques: Principles and application”, J. Chem. Phys., 124 (2006), 244704 |
6. |
K.Shiina, H.Mori, Y.Okabe, H.K.Lee, “Machine-Learning Studies on Spin Models”, Sci. Rep., 10 (2020), 2177 |
7. |
A.Azizi, M.Pleimling, “A cautionary tale for machine learning generated configurations in presence of a conserved quantity”, Sci. Rep., 11 (2021), 6395 |
8. |
A.Tanaka, A.Tomiya, “A cautionary tale for machine learning generated configurations in presence of a conserved quantity”, J. Phys. Soc. Jpn., 86 (2017), 063001 |
9. |
S.J.Wetzel, M.Scherzer, “Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory”, Phys. Rev. B, 96 (2017), 184410 |
10. |
N.Maskara, M.Buchhold, M.Endres, E. van Nieuwenburg, “Learning algorithm relecting universal scaling behavior near phase transitions”, Phys. Rev. Res., 4 (2022), L022032 |
11. |
D.A.Martin, T.L.Ribeiro, and etc., “Box-scaling as a proxy of finite-size correlations”, Sci. Rep., 11 (2020), 15937 |
12. |
Ju.V.Mikhailova, V.P.Krainov, R.O.Zaitsev, Two-dimensional Ising model (exact solution), MIPT, M., 2015 |
13. |
N.Metropolis, A.W.Rosenbluth, M.N.Rosenbluth, A.H.Teller, E.Teller, “Equations of State Calculations by Fast Computing Machines”, J. Chem. Phys., 21 (1953), 1087 |