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

Sonia Boscolo; John M. Dudley; Christophe Finot

doi:10.1016/j.rio.2021.100066

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

Sonia Boscolo, John M. Dudley, Christophe Finot

Aston Institute of Photonic Technologies (AiPT)

Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174, Besançon, 25000, France
Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université de Bourgogne-Franche-Comté, 9 avenue Alain Savary, BP 47870, 21078 Dijon Cedex, France

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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.

Original language	English
Article number	100066
Journal	Results in Optics
Volume	3
Early online date	16 Feb 2021
DOIs	https://doi.org/10.1016/j.rio.2021.100066
Publication status	Published - May 2021

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Creative Commons Attribution 4.0 International (CC BY 4.0)

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

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

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