We develop a recurrent neural network framework to model the non-Markovian dynamics exhibited by two-level atoms interacting with the radiation reservoir of a photonic crystal. Despite the strong non-Markovianity of the atomic dynamics induced by the rapid spectral variation in photonic density of states of the photonic reservoir, our recurrent neural network approach is able to capture precise details in the atomic evolution, including the fractional steady-state atomic population inversion and spectral splitting of the atomic transition. We demonstrate the robustness of the recurrent neural network setup against reduced data sets and its effectiveness to deal with systems of increased complexity.

中文翻译：

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用循环神经网络模拟光子晶体中的非马尔可夫动力学

我们开发了一个循环神经网络框架来模拟两级原子与光子晶体的辐射库相互作用所表现出的非马尔可夫动力学。尽管光子库的光子态密度的快速光谱变化引起了原子动力学的强烈非马尔可夫性，但我们的递归神经网络方法能够捕获原子演化中的精确细节，包括分数稳态原子群原子跃迁的反演和光谱分裂。我们证明了循环神经网络设置对减少的数据集的鲁棒性及其处理复杂性增加的系统的有效性。