Learning nonequilibrium statistical mechanics and dynamical phase transitions.

ABSTRACT: Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive n

SUBMITTER: Tang Y 

PROVIDER: S-EPMC10847122 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature