1. Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers
- Author
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Si, Zhi-Zeng, Wang, Da-Lei, Zhu, Bo-Wei, Ju, Zhen-Tao, Wang, Xue-Peng, Liu, Wei, Malomed, Boris A., Wang, Yue-Yue, and Dai, Chao-Qing
- Subjects
Physics - Optics ,Nonlinear Sciences - Pattern Formation and Solitons - Abstract
Soliton pulsations are ubiquitous feature of non-stationary soliton dynamics in mode-locked lasers and many other physical systems. To overcome difficulties related to huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non-stationary solitons, we propose a two-parallel bidirectional long short-term memory recurrent neural network, with the main objective to predict dynamics of vector-soliton pulsations in various complex states, whose real-time dynamics is verified by experiments. 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., Comment: To be published in Laser & Photonics Reviews;https://doi.org/10.1002/lpor.202400097
- Published
- 2024
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