Modelling non-Markovian dynamics in photonic crystals with recurrent neural networks

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.

Automated summary

The study introduces a recurrent neural network framework to model the dynamics of two-level atoms interacting with a photonic crystal, demonstrating its ability to capture details in atomic evolution despite strong non-Markovianity. The robustness of the recurrent neural network setup against reduced data sets and its effectiveness in dealing with increasingly complex systems is also showcased.