Abstract
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.
| Original language | English |
|---|---|
| Article number | 2200470 |
| Number of pages | 11 |
| Journal | Laser and Photonics Reviews |
| Early online date | 22 Nov 2023 |
| DOIs | |
| Publication status | E-pub ahead of print - 22 Nov 2023 |
Bibliographical note
For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arisingFunding
The National Natural Science Fund of China (11621404, 11561121003, 11727812, 61775059, and 11704123); the Key Project of Shanghai Education Commission (2017‐01‐07‐00‐05‐E00021); the Science and Technology Innovation Program of Basic Science Foundation of Shanghai (18JC1412000); the Shanghai Rising‐Star Program; Sustainedly Supported Foundation by National Key Laboratory of Science and Technology on Space Microwave under Grant 2022‐WDKY‐SYS‐DN‐04; Shanghai Natural Science Foundation (23ZR1419000). UK Engineering and Physical Sciences Research Council (EP/S003436/1, EP/X019241/1); French National Research Agency (ANR‐20‐CE30‐0004).
| Funders | Funder number |
|---|---|
| Basic Science Foundation of Shanghai | 18JC1412000 |
| Key Project of Shanghai Education Commission | 2017‐01‐07‐00‐05‐E00021 |
| National Key Laboratory of Science and Technology on Space Microwave | 2022‐WDKY‐SYS‐DN‐04 |
| Sustainedly Supported Foundation | |
| Natural Science Foundation of Shanghai Municipality | 23ZR1419000 |
| National Natural Science Foundation of China | 61775059, 11727812, 11704123, 11561121003, 11621404 |
| Shanghai Rising-Star Program |
Keywords
- machine learning
- mode locking
- rogue waves
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