Control of Spectral Extreme Events in Ultrafast Fiber Lasers by a Genetic Algorithm

• Published in: Laser & photonics reviews Vol. 18; no. 4
• Main Authors: Wu, Xiuqi, Zhang, Ying, Peng, Junsong, Boscolo, Sonia, Finot, Christophe, Zeng, Heping
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.04.2024; Wiley-VCH Verlag
• Subjects: Active control; Bose-Einstein condensates; Complex systems; Environment models; Fiber lasers; Genetic algorithms; machine learning; mode locking; Nonlinear optics; Nonlinear systems; Ocean models; Optics; Optimization; Physics; rogue waves
• ISSN: 1863-8880; 1863-8899
• DOI: 10.1002/lpor.202200470

Extreme wave events or rogue waves (RWs) are both statistically rare and of exceptionally large amplitude. They are observed in many complex systems ranging from oceanic and optical environments to financial models and Bose–Einstein condensates. As they appear from nowhere and disappear without a trace, their emergence is unpredictable and non‐repetitive, which makes them particularly challenging to control. Here, the use of genetic algorithms (GAs), which are exclusively designed for searching and optimizing stationary or repetitive processes in nonlinear optical systems, is extended to the active control of extreme events in a fiber laser cavity. Feeding real‐time spectral measurements into a GA controlling the electronics to optimize the cavity parameters, the wave events are able to be triggered in the cavity that have the typical statistics of RWs in the frequency domain. The intensity of the induced RWs can also be tailored. This accurate control enables the generation of optical RWs with a spectral peak intensity 32.8 times higher than the significant intensity threshold. A rationale is proposed and confirmed by numerical simulations of the laser model for the related frequency up‐ and downshifting of the optical spectrum that are experimentally observed. Rogue waves (also known as freak waves and monster waves) are unpredictable strong destructive waves existing widely in nature and science, and are rather difficult to be tamed. Here, using a fiber laser as a test bed, this work demonstrates that genetic algorithms can be employed to tame these monster waves, and their novel generation mechanisms are revealed.