Modelling self-similar parabolic pulses in optical fibres with a neural network

TL;DR

This paper employs neural networks to model and generate self-similar parabolic pulses in optical fibers, effectively solving direct and inverse pulse shaping problems without traditional numerical simulations.

Contribution

It introduces a neural network approach to model nonlinear pulse shaping in optical fibers with gain/loss, extending previous work to self-similar parabolic pulses.

Findings

Neural network accurately predicts pulse evolution in fibers.

Method simplifies pulse shaping process in optical communications.

Enables inverse design of pulse profiles.

Abstract

We expand our previous analysis of nonlinear pulse shaping in optical fibres using machine learning [Opt. Laser Technol., 131 (2020) 106439] to the case of pulse propagation in the presence of gain/loss, with a special focus on the generation of self-similar parabolic pulses. We use a supervised feedforward neural network paradigm to solve the direct and inverse problems relating to the pulse shaping, bypassing the need for direct numerical solution of the governing propagation model.