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

• Published in: arXiv.org
• Main Authors: Zhi-Zeng Si, Da-Lei, Wang, Bo-Wei, Zhu, Zhen-Tao Ju, Xue-Peng, Wang, Liu, Wei, Malomed, Boris A, Yue-Yue, Wang, Chao-Qing, Dai
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 05.08.2024
• Subjects: Deep learning; Dynamic models; Fiber lasers; Information storage; Laser mode locking; Numerical methods; Physics - Optics; Physics - Pattern Formation and Solitons; Real time; Recurrent neural networks; Solitary waves
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2407.18725

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.