Mabed Mehdi, Meng Fanchao, Salmela Lauri, Finot Christophe, Genty Goëry, Dudley John M
Opt Express. 2022 Apr 25;30(9):15060-15072. doi: 10.1364/OE.455945.
Neural networks have been recently shown to be highly effective in predicting time-domain properties of optical fiber instabilities based only on analyzing spectral intensity profiles. Specifically, from only spectral intensity data, a suitably trained neural network can predict temporal soliton characteristics in supercontinuum generation, as well as the presence of temporal peaks in modulation instability satisfying rogue wave criteria. Here, we extend these previous studies of machine learning prediction for single-pass fiber propagation instabilities to the more complex case of noise-like pulse dynamics in a dissipative soliton laser. Using numerical simulations of highly chaotic behaviour in a noise-like pulse laser operating around 1550 nm, we generate large ensembles of spectral and temporal data for different regimes of operation, from relatively narrowband laser spectra of 70 nm bandwidth at the -20 dB level, to broadband supercontinuum spectra spanning 200 nm at the -20 dB level and with dispersive wave and long wavelength Raman extension spanning from 1150-1700 nm. Using supervised learning techniques, a trained neural network is shown to be able to accurately correlate spectral intensity profiles with time-domain intensity peaks and to reproduce the associated temporal intensity probability distributions.
最近研究表明，仅通过分析光谱强度分布，神经网络就能非常有效地预测光纤不稳定性的时域特性。具体而言，仅利用光谱强度数据，经过适当训练的神经网络就能预测超连续谱产生中的时间孤子特性，以及满足 rogue 波标准的调制不稳定性中时间峰值的存在情况。在此，我们将之前关于单通光纤传播不稳定性的机器学习预测研究扩展到耗散孤子激光器中更复杂的类噪声脉冲动力学情况。通过对工作在 1550 nm 附近的类噪声脉冲激光器中的高度混沌行为进行数值模拟，我们针对不同的工作状态生成了大量的光谱和时域数据，从 -20 dB 水平下带宽为 70 nm 的相对窄带激光光谱，到 -20 dB 水平下跨度为 200 nm 且具有从 1150 - 1700 nm 的色散波和长波长拉曼扩展的宽带超连续谱。使用监督学习技术，结果表明经过训练的神经网络能够准确地将光谱强度分布与时域强度峰值相关联，并重现相关的时域强度概率分布。
