Predicting frequency comb structure in nonlinear optical fibre using a neural network

Abstract

We deploy a neural network to predict the spectro-temporal evolution of simple sinusoidal temporal modulations upon propagation in a nonlinear dispersive fibre. Thanks to the speed of the neural network, we can efficiently scan the input parameter space for the generation of on-demand frequency combs or the occurrence of substantial spectral/temporal focusing.

Publication DOI: https://doi.org/10.1051/epjconf/202328706019
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Additional Information: © The Authors, published by EDP Sciences, 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication ISSN: 2100-014X
Last Modified: 27 Dec 2023 09:53
Date Deposited: 24 Oct 2023 10:37
Full Text Link:
Related URLs: https://www.epj ... 2023_06019.html (Publisher URL)
PURE Output Type: Conference article
Published Date: 2023-10-18
Accepted Date: 2023-09-01
Authors: Boscolo, Sonia (ORCID Profile 0000-0001-5388-2893)
Dudley, John M.
Finot, Christophe

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