论文标题
使用图神经网络学习Rydberg原子的自组织批判性
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks
论文作者
论文摘要
自组织的临界性（SOC）是一种普遍存在的动力学现象，被认为是许多看似无关的系统（例如森林火灾，病毒传播或原子激发动力学）中普遍规模不变行为的出现。 SOC描述了仅由于局部相互作用和耗散而导致的大规模和远程时空相关性的建立。 SOC动力学的仿真通常基于蒙特卡洛（MC）方法，但是在数值上很昂贵，并且并未超出某些系统尺寸。我们研究了图形神经网络（GNN）作为一个有效的替代模型，以学习范式SOC系统的动力运算符，灵感来自实验可访问的物理示例：驱动的Rydberg Atoms。为此，我们概括了现有的GNN仿真方法，以预测节点内部状态的动力学。我们表明，我们可以准确地重现MC动力学，并沿两个重要的颗粒数和粒子密度的轴概括。这为超出传统MC方法限制的更大系统建模的方式铺平了道路。虽然确切的系统灵感来自Rydberg Atoms的动力学，但该方法非常通用，并且可以轻松地应用于其他系统。
Self-Organized Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior in many, seemingly unrelated systems, such as forest fires, virus spreading or atomic excitation dynamics. SOC describes the buildup of large-scale and long-range spatio-temporal correlations as a result of only local interactions and dissipation. The simulation of SOC dynamics is typically based on Monte-Carlo (MC) methods, which are however numerically expensive and do not scale beyond certain system sizes. We investigate the use of Graph Neural Networks (GNNs) as an effective surrogate model to learn the dynamics operator for a paradigmatic SOC system, inspired by an experimentally accessible physics example: driven Rydberg atoms. To this end, we generalize existing GNN simulation approaches to predict dynamics for the internal state of the node. We show that we can accurately reproduce the MC dynamics as well as generalize along the two important axes of particle number and particle density. This paves the way to model much larger systems beyond the limits of traditional MC methods. While the exact system is inspired by the dynamics of Rydberg atoms, the approach is quite general and can readily be applied to other systems.
