Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
Statistical Mechanics
2021-09-22 v1 Disordered Systems and Neural Networks
Machine Learning
Abstract
We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled cumulant-generating function for the dynamical activity of the Fredrickson-Andersen model, a prototypical kinetically constrained model, in one and two dimensions, and present the first size-scaling analysis of the dynamical activity in two dimensions. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.
Keywords
Cite
@article{arxiv.2011.08657,
title = {Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz},
author = {Corneel Casert and Tom Vieijra and Stephen Whitelam and Isaac Tamblyn},
journal= {arXiv preprint arXiv:2011.08657},
year = {2021}
}