Intelligent breathing soliton generation in ultrafast fiber lasers

Abstract

Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a specific mode-locked regime generally involves adjusting multiple control parameters, in connection with a wide range of accessible pulse dynamics. Machine-learning tools have recently shown promising for the design of smart lasers that can tune themselves to desired operating states. Yet, machine-learning algorithms are mainly designed to target regimes of parameter-invariant, stationary pulse generation, while the intelligent excitation of evolving pulse patterns in a laser remains largely unexplored. Breathing solitons exhibiting periodic oscillatory behavior, emerging as ubiquitous mode-locked regime of ultrafast fiber lasers, are attracting considerable interest by virtue of their connection with a range of important nonlinear dynamics, such as exceptional points, and the Fermi-Pasta-Ulam paradox. Here, an evolutionary algorithm is implemented for the self-optimization of the breather regime in a fiber laser mode-locked through a four-parameter nonlinear polarization evolution. Depending on the specifications of the merit function used for the optimization procedure, various breathing-soliton states are obtained, including single breathers with controllable oscillation period and breathing ratio, and breather molecular complexes with a controllable number of elementary constituents. This work opens up a novel avenue for exploration and optimization of complex dynamics in nonlinear systems.

Publication DOI: https://doi.org/10.1002/lpor.202100191
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: This is the peer reviewed version of the following article: Wu, X., Peng, J., Boscolo, S., Zhang, Y., Finot, C., Zeng, H., Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers. Laser & Photonics Reviews 2021, which has been published in final form at https://doi.org/10.1002/lpor.202100191. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited Funding: National Key Research and Development Program. Grant Number: 2018YFB0407100 National Natural Science Fund of China. Grant Numbers: 11621404, 11561121003, 11727812, 61775059, 11704123 Key Project of Shanghai Education Commission. Grant Number: 2017-01-07-00-05-E00021 Science and Technology Innovation Program of Basic Science Foundation of Shanghai. Grant Number: 18JC1412000 Shanghai Rising-Star Program National Key Laboratory Foundation of China. Grant Number: 6142411196307 UK Engineering and Physical Sciences Research Council. Grant Number: EP/S003436/1
Publication ISSN: 1863-8899
Last Modified: 12 Jun 2024 07:23
Date Deposited: 13 Dec 2021 15:32
Full Text Link:
Related URLs: https://onlinel ... /lpor.202100191 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-02
Published Online Date: 2021-12-13
Accepted Date: 2021-09-19
Authors: Wu, Xiuqi
Peng, Junsong
Boscolo, Sonia (ORCID Profile 0000-0001-5388-2893)
Zhang, Ying
Finot, Christophe
Zeng, Heping

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record