Dynamical Phase Transitions in a 2D Classical Nonequilibrium Model via 2D Tensor Networks
Phillip Helms, Garnet Kin‐Lic Chan
Abstract
We demonstrate the power of 2D tensor networks for obtaining large deviation functions of dynamical observables in a classical nonequilibrium setting. Using these methods, we analyze the previously unstudied dynamical phase behavior of the fully 2D asymmetric simple exclusion process with biases in both the x and y directions. We identify a dynamical phase transition, from a jammed to a flowing phase, and characterize the phases and the transition, with an estimate of the critical point and exponents.
Topics & Concepts
ObservableNon-equilibrium thermodynamicsPhysicsStatistical physicsPhase transitionTensor (intrinsic definition)Phase (matter)Critical exponentCritical point (mathematics)Dynamical systems theorySimple (philosophy)Quantum mechanicsMathematical analysisMathematicsPure mathematicsPhilosophyEpistemologyAdvanced Thermodynamics and Statistical MechanicsStochastic processes and statistical mechanicsQuantum many-body systems