Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second -order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.

Automated summary

We utilize a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train with a comb structure in the frequency domain, resulting from nonlinear propagation in an optical fiber. We study both normal and anomalous second-order dispersion regimes of the fiber, and leverage the speed of the neural network to explore the input parameter space for generating customized combs or significant temporal and spectral focusing.