论文标题
通过2D张量网络在2D经典非平衡模型中的动力相变
Dynamical Phase Transitions in a 2D Classical Nonequilibrium Model via 2D Tensor Networks
论文作者
论文摘要
我们演示了2D张量网络在经典非平衡设置中获得动力学可观察物的大偏差函数的功率。使用这些方法，我们分析了以前未研究的完全2D不对称的简单排除过程的动力学相行为，并在X和Y方向上均具有偏差。我们确定了从堵塞到流动阶段的动态相变，并通过临界点和指数的估计来表征相位和过渡。
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.
