Machine learning for ultrafast nonlinear photonics

Abstract

We review our recent progress on the application of machine-learning techniques in the field of ultrafast nonlinear fibre optics. We demonstrate that neural networks can both efficiently predict the temporal and spectral features of optical signals that are obtained after propagation in the presence of focusing and defocusing nonlinearity and solve the associated inverse problem. We also show that evolutionary algorithms can be used to control complex nonlinear dynamics of ultrafast fibre lasers.

Publication DOI: https://doi.org/10.1109/ICLO54117.2022
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
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Event Title: 20th International Conference Laser Optics
Event Type: Other
Event Dates: 2022-06-20 - 2022-06-24
ISBN: 978-1-6654-6664-6
Last Modified: 03 Jun 2024 08:03
Date Deposited: 19 Aug 2022 09:56
PURE Output Type: Chapter (peer-reviewed)
Published Date: 2022-06
Authors: Finot, Christophe
Sheveleva, Anastasiia
Peng, Junsong
Dudley, John M.
Boscolo, Sonia (ORCID Profile 0000-0001-5388-2893)

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