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arXiv · 2407.18725

Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers

Zhi-Zeng Si·Da-Lei Wang·Bo-Wei Zhu·Zhen-Tao Ju·Xue-Peng Wang·Wei Liu·Boris A. Malomed·Yue-Yue Wang·Chao-Qing Dai
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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.

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Zhi-Zeng Si, Da-Lei Wang, Bo-Wei Zhu, Zhen-Tao Ju, Xue-Peng Wang, Wei Liu, Boris A. Malomed, Yue-Yue Wang, Chao-Qing Dai. 2024-07-26. Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers. https://doi.org/10.1002/lpor.202400097

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