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)

Research output: Contribution to journal › Article › peer-review

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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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.",

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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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