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.

Publication DOI: https://doi.org/10.1016/j.optcom.2023.129563
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: Copyright © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Neural networks,Nonlinear propagation,Optical fibres,Pulse shaping,Electronic, Optical and Magnetic Materials,Atomic and Molecular Physics, and Optics,Electrical and Electronic Engineering,Physical and Theoretical Chemistry
Publication ISSN: 1873-0310
Last Modified: 13 Jun 2024 07:32
Date Deposited: 12 May 2023 15:14
Full Text Link:
Related URLs: https://www.sci ... 3103?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-09-01
Published Online Date: 2023-05-05
Accepted Date: 2023-04-28
Authors: Boscolo, Sonia (ORCID Profile 0000-0001-5388-2893)
Dudley, John M.
Finot, Christophe

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