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We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symmetry-breaking phase transitions in considered systems irrespective of the universality class, order, and the presence of discrete or continuous degrees of freedom.
1903
Earlier work this paper cites.
R. Baxter, Exactly solved models in statistical mechanics (1982)
1982
Earlier work this paper cites.
A. Milchev, D. W. Heermann, and K. Binder, Finite-size scaling analysis of the ϕ \phi 4 field theory on the square lattice, Journal of Statistical Physics 44
1986
Earlier work this paper cites.
A. M. Ferrenberg and R. H. Swendsen, New monte carlo technique for studying phase transitions, Phys. Rev. Lett. 61
1988
Earlier work this paper cites.
A. M. Ferrenberg and R. H. Swendsen, Optimized monte carlo data analysis, Phys. Rev. Lett. 63
1989
Earlier work this paper cites.
U. Wolff, Collective monte carlo updating for spin systems, Phys. Rev. Lett. 62
1989
Earlier work this paper cites.
R. C. Brower and P. Tamayo, Embedded dynamics for φ 4 {\mathrm{\varphi}}^{4} theory, Phys. Rev. Lett. 62
1989
Earlier work this paper cites.
W. Loinaz and R. S. Willey, Monte carlo simulation calculation of the critical coupling constant for two-dimensional continuum φ 4 {\varphi}^{4} theory, Phys. Rev. D 58
1998
Earlier work this paper cites.
M. E. J. Newman and G. T. Barkema, Monte Carlo methods in statistical physics (Clarendon Press, Oxford, 1999)
1999
Earlier work this paper cites.
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
D. Schaich and W. Loinaz, Improved lattice measurement of the critical coupling in ϕ 2 4 {\phi}_{2}^{4} theory, Phys. Rev. D 79
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Trans. on Knowl. and Data Eng. 22
2010
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, How transferable are features in deep neural networks?, in Advances in Neural Information Processing Systems 27 , edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Curran Associates, Inc., 2014) pp. 3320–3328
2014
Earlier work this paper cites.
I. J. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, Cambridge, MA, USA, 2016) http://www.deeplearningbook.org
2016
Earlier work this paper cites.
G. Torlai and R. G. Melko, Learning thermodynamics with boltzmann machines, Phys. Rev. B 94
2016
Earlier work this paper cites.
L. Wang, Discovering phase transitions with unsupervised learning, Phys. Rev. B 94
2016
Cited alongside, same era.
J. Carrasquilla and R. G. Melko, Machine learning phases of matter, Nature Physics 13
2017
Cited alongside, same era.
E. L. van Nieuwenburg, Y.-H. Liu, and S. Huber, Learning phase transitions by confusion, Nature Physics 13
2017
Cited alongside, same era.
P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, Machine learning quantum phases of matter beyond the fermion sign problem, Scientific Reports 7
2017
Cited alongside, same era.
K. Ch’ng, J. Carrasquilla, R. G. Melko, and E. Khatami, Machine learning phases of strongly correlated fermions, Phys. Rev. X 7
2017
Cited alongside, same era.
P. Huembeli, A. Dauphin, and P. Wittek, Identifying quantum phase transitions with adversarial neural networks, Phys. Rev. B 97
2018
Later among the works it cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Reviews of Modern Physics 91
2019
Later among the works it cites.
Q. Ni, M. Tang, Y. Liu, and Y.-C. Lai, Machine learning dynamical phase transitions in complex networks, Phys. Rev. E 100
2019
Later among the works it cites.
K. Zhou, G. Endrődi, L.-G. Pang, and H. Stöcker, Regressive and generative neural networks for scalar field theory, Phys. Rev. D 100
2019
Later among the works it cites.
J. F. Rodriguez-Nieva and M. S. Scheurer, Identifying topological order through unsupervised machine learning, Nature Physics 15
2019
Later among the works it cites.
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2017
Cited alongside, same era.
P. Ponte and R. G. Melko, Kernel methods for interpretable machine learning of order parameters, Phys. Rev. B 96
2017
Cited alongside, same era.
W. Hu, R. R. P. Singh, and R. T. Scalettar, Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination, Phys. Rev. E 95
2017
Cited alongside, same era.
C. Wang and H. Zhai, Machine learning of frustrated classical spin models. i. principal component analysis, Phys. Rev. B 96
2017
Cited alongside, same era.
N. C. Costa, W. Hu, Z. J. Bai, R. T. Scalettar, and R. R. P. Singh, Principal component analysis for fermionic critical points, Phys. Rev. B 96
2017
Cited alongside, same era.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355
2017
Cited alongside, same era.
P. Suchsland and S. Wessel, Parameter diagnostics of phases and phase transition learning by neural networks, Phys. Rev. B 97
2018
Cited alongside, same era.
C. Giannetti, B. Lucini, and D. Vadacchino, Machine learning as a universal tool for quantitative investigations of phase transitions, Nuclear Physics B 944
2019
Later among the works it cites.
J. Greitemann, K. Liu, and L. Pollet, Probing hidden spin order with interpretable machine learning, Phys. Rev. B 99
2019
Later among the works it cites.
K. Liu, J. Greitemann, and L. Pollet, Learning multiple order parameters with interpretable machines, Phys. Rev. B 99
2019
Later among the works it cites.
W. Zhang, L. Wang, and Z. Wang, Interpretable machine learning study of the many-body localization transition in disordered quantum ising spin chains, Phys. Rev. B 99
2019
Later among the works it cites.
A. Canabarro, F. F. Fanchini, A. L. Malvezzi, R. Pereira, and R. Chaves, Unveiling phase transitions with machine learning, Phys. Rev. B 100
2019
Later among the works it cites.
M. N. Chernodub, H. Erbin, V. A. Goy, and A. V. Molochkov, Topological defects and confinement with machine learning: The case of monopoles in compact electrodynamics, Phys. Rev. D 102
2020
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2020
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2020
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