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Area of Science:

  • Statistical Physics
  • Machine Learning
  • Computational Physics

Background:

  • Dynamical large deviations are crucial for understanding complex systems.
  • Neural network methods have shown promise in physics applications.

Purpose of the Study:

  • To adapt a neural-network ansatz for studying classical dynamical large deviations.
  • To investigate the applicability of recurrent neural networks in modeling glass dynamics.

Main Methods:

  • Utilized a neural-network ansatz for variational optimization.
  • Employed recurrent neural networks to model dynamical activity in 2D kinetically constrained models.
  • Performed finite size-scaling analysis on the Fredrickson-Andersen model.
  • Explored spatial structures in the South-or-East model.

Main Results:

  • Successfully applied neural networks to analyze large deviations in classical systems.
  • Presented the first finite size-scaling analysis of large-deviation functions for the 2D Fredrickson-Andersen model.
  • Characterized the high-activity sector of the South-or-East model.

Conclusions:

  • The neural-network state ansatz offers a novel approach to studying dynamical large deviations.
  • This methodology demonstrates broad applicability across different physics domains.