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Supervised, semisupervised, and unsupervised learning of the Domany-Kinzel model.

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Machine learning methods reveal critical points and exponents in the Domany-Kinzel (DK) model

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Area of Science:

  • Complex systems
  • Statistical physics
  • Machine learning

Background:

  • The Domany-Kinzel (DK) model exhibits diverse nonequilibrium phase transitions.
  • Understanding critical behaviors in such models is crucial for statistical physics.

Purpose of the Study:

  • To apply machine learning techniques for analyzing phase transitions in the (1+1)-dimensional DK model.
  • To estimate critical points and correlation exponents using various learning methods.

Main Methods:

  • Supervised, semisupervised, and unsupervised learning approaches were employed.
  • Principal Component Analysis (PCA) and autoencoders were utilized for critical point prediction.

Main Results:

  • Supervised and semisupervised learning accurately estimated critical points and correlation exponents.
  • PCA and autoencoders showed good agreement with simulated particle number density for critical point prediction.

Conclusions:

  • Machine learning offers powerful tools for studying phase transitions in complex models like the DK model.
  • These methods provide accurate estimations of critical phenomena, even with unlabeled data.