Sciact
  • EN
  • RU

Multistability Manipulation by Reinforcement Learning Algorithm Inside Mode-Locked Fiber Laser Научная публикация

Журнал Nanophotonics
ISSN: 2192-8614
Вых. Данные Год: 2024, Том: 13, Номер: 16, Страницы: 2891–2901 Страниц : 11 DOI: 10.1515/nanoph-2023-0792
Ключевые слова multistability; harmonic mode-locked lasers; reinforcement learning; single wall carbon nanotubes; saturable absorber
Авторы Kokhanovskiy Alexey 1 , Kuprikov Evgeny 2 , Serebrennikov Kirill 2,3 , Mkrtchyan Aram 4 , Davletkhanov Ayvaz 4 , Bunkov Alexey 4 , Krasnikov Dmitry 4 , Shashkov Mikhail 5 , Nasibulin Albert 4 , Gladush Yuriy 4
Организации
1 School of Physics and Engineering, ITMO University, St. Petersburg 197101, Russia
2 Novosibirsk State University, Pirogova 2, Novosibirsk 630090, Russia
3 Institute of Automation and Electrometry SB RAS, 1 Ac. Koptyug ave., Novosibirsk 630090, Russia
4 Skolkovo Institute of Science and Technology, Moscow 121205, Russia
5 Boreskov Institute of Catalysis SB RAS, Novosibirsk 630090, Russia

Информация о финансировании (2)

1 Российский научный фонд 20-73-10256 (122042700104-7)
2 Министерство науки и высшего образования Российской Федерации (с 15 мая 2018) 075-03-2024-295 (124041700065-2)(FWNG-2024-0015)

Реферат: Fiber mode-locked lasers are nonlinear optical systems that provide ultrashort pulses at high repetition rates. However, adjusting the cavity parameters is often a challenging task due to the intrinsic multistability of a laser system. Depending on the adjustment of the cavity parameters, the optical output may vary significantly, including Q-switching, single and multipulse, and harmonic mode-locked regimes. In this study, we demonstrate an experimental implementation of the Soft Actor–Critic algorithm for generating a harmonic mode-locked regime inside a state-of-the-art fiber laser with an ion-gated nanotube saturable absorber. The algorithm employs nontrivial strategies to achieve a guaranteed harmonic mode-locked regime with the highest order by effectively managing the pumping power of a laser system and the nonlinear transmission of a nanotube absorber. Our results demonstrate a robust and feasible machine-learning–based approach toward an automatic system for adjusting nonlinear optical systems with the presence of multistability phenomena.
Библиографическая ссылка: Kokhanovskiy A. , Kuprikov E. , Serebrennikov K. , Mkrtchyan A. , Davletkhanov A. , Bunkov A. , Krasnikov D. , Shashkov M. , Nasibulin A. , Gladush Y.
Multistability Manipulation by Reinforcement Learning Algorithm Inside Mode-Locked Fiber Laser
Nanophotonics. 2024. V.13. N16. P.2891–2901. DOI: 10.1515/nanoph-2023-0792 WOS Scopus РИНЦ OpenAlex
Даты:
Поступила в редакцию: 9 нояб. 2023 г.
Принята к публикации: 3 апр. 2024 г.
Опубликована online: 15 апр. 2024 г.
Опубликована в печати: 1 июл. 2024 г.
Идентификаторы БД:
Web of science: WOS:001202678000001
Scopus: 2-s2.0-85190723390
РИНЦ: 67230888
OpenAlex: W4394819420
Цитирование в БД:
БД Цитирований
OpenAlex 1
Web of science 2
Scopus 2
Альметрики: