- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Extreme Events Prediction in Optical Fibre Modulation Instability using Machine Learning
摘要： The study of instabilities that drive extreme events is central to nonlinear science. One of the most celebrated example of nonlinear instability is modulation instability (MI) which describes the exponential amplification of noise on top of an input signal. When seeded by noise, MI has been shown to be associated with the emergence of high intensity localized temporal breathers with random statistics and it has also been suggested that MI may be linked to the formation of extreme events or rogue waves [1]. Real-time techniques such as the dispersive Fourier transform (DFT) are commonly used to measure ultrafast instabilities [2]. Although conceptually simple and easy to implement, the DFT only provides spectral information, limiting the knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, we train a supervised neural network (NN) to correlate the spectral and temporal properties of modulation instability using numerical simulations, and then we apply the neural network model to analyse high dynamic range experimental MI spectra to yield the probability distribution for the highest temporal peaks in the instability field [3].
关键词： modulation instability,optical fibre,machine learning,neural network,extreme events
更新于2025-09-12 10:27:22
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Extreme Events Generation via Cascaded Stimulated Brillouin Scattering in Self-Pulsing Fiber Lasers
摘要： Extreme-amplitude events and rare instabilities are observed for more than a decade in various optical systems. Specifically in dissipative systems, such as Raman fiber lasers, laser diodes or mode-locked lasers, to name a few, long-tailed statistics and highly-localized temporal structures have been observed [1,2]. Recent studies showed that stimulated Brillouin scattering (SBS) can also trigger the generation of extreme events in various configurations, from self-pulsing fiber lasers [3,4] to Q-switched random fiber lasers [5]. It is indeed known that the stochastic nature of SBS can promote the emergence of randomly distributed giant pulses which can induce irreversible damages in fiber laser systems. In order to understand and open the possibility to harness such extreme events, numerical models have then to be developed and refined. In the context of self-pulsed fiber lasers, a few studies taking into account only one or two fundamental Stokes orders have already been reported [6]. Such models describe well the large pulse-to-pulse intensity fluctuations observed in Erbium-doped fiber lasers Q-switched through SBS but they however did not predict any extreme event [6]. We propose here an extension of the model proposed in Ref. [3] by generalizing it to higher Stokes orders to study their impact on extreme dynamics in a self-pulsing laser. Our model is based on the coupled amplitudes equations describing the spatiotemporal dynamics of both the laser and Brillouin waves with their corresponding acoustic fields, as well as the temporal variations of the gain for each wave. We also consider a matter equation to account for the saturable absorption effect which can occur in the un-pumped segment of the active fiber. We show that increasing the number of SBS orders interacting with the gain medium reveals new dynamics enabling the generation of extreme events which are not predicted by the single SBS order model. Pulses with amplitudes 27 times the so-called significant wave height are indeed predicted, which attest of the presence of extreme-amplitude events, as shown in Fig. 1. Such giant pulses could then reach the threshold of irreversible damage in optical fibers. We also provide a comprehensive study on the different parameters influencing the dynamics, including the number of Stokes orders that strongly affect the laser dynamical behavior and then allow to somehow control the highest intensity of the laser instabilities. The influence of other parameters, such as the input noise, on the system’s dynamics will be discussed and we will show that our simplified model can pave the way towards a better understanding of the complex stochastic dynamics under the influence of SBS.
关键词： stimulated Brillouin scattering,stochastic dynamics,Extreme events,optical systems,self-pulsing fiber lasers
更新于2025-09-11 14:15:04
-
Machine learning analysis of extreme events in optical fibre modulation instability
摘要： A central research area in nonlinear science is the study of instabilities that drive extreme events. Unfortunately, techniques for measuring such phenomena often provide only partial characterisation. For example, real-time studies of instabilities in nonlinear optics frequently use only spectral data, limiting knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, a supervised neural network is trained to correlate the spectral and temporal properties of modulation instability using simulations, and then applied to analyse high dynamic range experimental spectra to yield the probability distribution for the highest temporal peaks in the instability field. We also use unsupervised learning to classify noisy modulation instability spectra into subsets associated with distinct temporal dynamic structures. These results open novel perspectives in all systems exhibiting instability where direct time-domain observations are difficult.
关键词： machine learning,extreme events,optical fibre modulation instability,unsupervised learning,supervised neural network
更新于2025-09-10 09:29:36