Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
This work provides a new method for studying dynamical large-deviation functions, which is important for researchers in statistical physics and complex systems.
This paper applies a neural network ansatz, originally for quantum systems, to classical systems to study dynamical large deviations. They successfully calculated the scaled cumulant-generating function for the dynamical activity of the Fredrickson-Andersen model in one and two dimensions, and performed the first size-scaling analysis of dynamical activity in two dimensions.
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.