论文标题
使用神经网络状态ANSATZ的二维动力学约束模型的动力大偏差
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
论文作者
论文摘要
我们使用最初设计用于量子系统变化优化的神经网络ANSATZ来研究经典的偏差。我们为Fredrickson-Andersen模型的动力活性获得了缩放的累积生成函数，Fredrickson-Andersen模型是一个典型的动力学约束模型，在一个维度和二维中，并介绍了两个维度中动态活性的第一个尺寸缩放分析。这些结果为研究动态大泄漏函数的研究提供了新的途径，并突出了跨物理领域的神经网络状态ANSATZ的广泛适用性。
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.
