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Learning algorithm reflecting universal scaling behavior near phase transitions

N. Maskara1,2,*, M. Buchhold1,3, M. Endres1, and E. van Nieuwenburg1,4

  • 1Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, California 91125, USA
  • 2Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
  • 3Institute for Theoretical Physics, University of Cologne, Zülpicher Strasse 77, 50937 Cologne, Germany
  • 4Niels Bohr International Academy, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark

  • *nmaskara@g.harvard.edu

A machine-learning based approach to the evaluation of the critical scaling behavior of anisotropic spin systems

Alina A Chubarova, Ivan A Mamonov, Marina V Mamonova, Mikhail I Bogachev, and Pavel V Prudnikov

J. Phys.: Condens. Matter 38 395403 (2026)

Capturing the dynamics of the phase transition of skyrmions with a nonstationary machine learning approach

Long Xiong, Neng-Ji Zhou, Shi-Qian Hu, Lei-Lei Nian, and Bo Zheng

Phys. Rev. B 111 184415 (2025)

Finding critical points and correlation length exponents using finite size scaling of Gini index

Soumyaditya Das, Soumyajyoti Biswas, Anirban Chakraborti, and Bikas K. Chakrabarti

Phys. Rev. E 109 024121 (2024)

Critical Scaling through Gini Index

Soumyaditya Das and Soumyajyoti Biswas

Phys. Rev. Lett. 131 157101 (2023)

Energy transfer in random-matrix ensembles of Floquet Hamiltonians

Christina Psaroudaki and Gil Refael

Phys. Rev. B 108 064301 (2023)

Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions

Julian Arnold and Frank Schäfer

Phys. Rev. X 12 031044 (2022)

Experimental demonstration of adversarial examples in learning topological phases

Huili Zhang, Si Jiang, Xin Wang, Wengang Zhang, Xianzhi Huang, Xiaolong Ouyang, Yefei Yu, Yanqing Liu, Dong-Ling Deng, and L.-M. Duan

Nat Commun 13 4993 (2022)

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