Deep Learning for Dynamic Modeling and Coded Information Storage of Vector‐Soliton Pulsations in Mode‐Locked Fiber Lasers

• Published in: Laser & photonics reviews Vol. 18; no. 12
• Main Authors: Si, Zhi‐Zeng, Wang, Da‐Lei, Zhu, Bo‐Wei, Ju, Zhen‐Tao, Wang, Xue‐Peng, Liu, Wei, Malomed, Boris A., Wang, Yue‐Yue, Dai, Chao‐Qing
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.12.2024
• Subjects: coded information storage; Computer simulation; Deep learning; Dynamic models; Fiber lasers; Information storage; Machine learning; Neural networks; Numerical methods; Predictions; real‐time dynamics; Recurrent neural networks; Simulation; Solitary waves; TP‐Bi_LSTM RNN; vector‐soliton pulsations
• ISSN: 1863-8880; 1863-8899
• DOI: 10.1002/lpor.202400097

Soliton pulsations are ubiquitous feature of non‐stationary soliton dynamics in mode‐locked lasers and many other physical systems. To overcome difficulties related to a huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non‐stationary solitons, a two‐parallel bidirectional long short‐term memory recurrent neural network (TP‐Bi_LSTM RNN) is proposed, with the main objective to predict dynamics of vector‐soliton pulsations (VSPs) in various complex states, whose real‐time dynamics is verified by experiments. For two examples, viz., single‐ and bi‐periodic VSPs, with period‐21 and a combination of period‐3 and period‐43, the prediction results are better than provided by direct simulations – namely, deviations produced by the TP‐Bi_LSTM RNN results are 36% and 18% less than those provided by the simulations, respectively. This means that predicted results provided by the neural network are better than numerical simulations. Moreover, the prediction results for unstable VSP state with period‐9 indicate that the optimization of training sets and the number of training iterations are particularly important for the predictability. Besides, the scheme of coded information storage based on the TP‐Bi_LSTM RNN, instead of actual pulse signals, is realized too. The findings offer new applications of deep learning to ultrafast optics and information storage. This work proposes a two‐parallel bidirectional long short‐term memory recurrent neural network (TP‐Bi_LSTM RNN), and successfully predicts the vector‐soliton pulsations in stable and unstable forms from non‐stationary soliton dynamics solely by relying on initial data. Besides, the work offers a new scheme of coded information storage based on the TP‐Bi_LSTM RNN rather than the actual pulse signal.