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

Abstract

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.

References in corpus (5)

• Optical frequency comb technology for ultra-broadband radio-frequency photonics
• Deep learning neural networks for the third-order nonlinear Schrodinger equation: Solitons, breathers, and rogue waves
• Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method
• Phase space topology of four-wave mixing reconstructed by a neural network
• Exploring Fresnel diffraction at a straight edge with a neural network