Machine learning control of complex nonlinear dynamics in fibre lasers

Abstract

We review our recent work on the use of genetic algorithms to control non-stationary nonlinear wave dynamics in ultrafast fibre lasers, including the generation of breathing-soliton dynamics with controlled characteristics, the disclosure of the fractal dynamics of breathers, and the generation of rogue waves with controlled intensity.

Publication DOI: https://doi.org/10.1051/epjconf/202328706001
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: 29 Oct 2024 15:58
Date Deposited: 26 Oct 2023 12:33
Full Text Link:
Related URLs: https://www.epj ... 2023_06001.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)
Peng, Junsong
Wu, Xiuqi
Zhang, Ying
Finot, Christophe
Zeng, Heping

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