Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks
Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks
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DOI：
10.1364/ome.425263
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发表时间：
2021-07-01
影响因子：
2.8
通讯作者：
Florescu, Marian
中科院分区：
文献类型：
--
作者：
Burgess, Adam;Florescu, Marian
我们发展了一个递归神经网络框架来模拟二能级原子与光子晶体辐射库相互作用所表现出的非马尔可夫动力学。尽管由于光子库的光子态密度的快速变化导致原子动力学具有很强的非马尔可夫性，但是我们的递归神经网络方法能够准确地捕捉原子演化的细节，包括分数稳态原子布居反转和原子跃迁的光谱分裂。我们证明了递归神经网络对减少的数据集的稳健性，以及它对处理复杂性增加的系统的有效性。
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.