Learning to predict soliton dynamics in fiber lasers using a recurrent neural network with high accuracy
Year: 2024
Type: article
Abstract: Accurate and efficient prediction of ultrafast dynamics for a mode-locked fiber laser (MLFL) utilizing deep learning is important for understanding the fundamental physics in ultrafast science and facilitating applications of fiber laser. However, it is still challenging to accurately predict the pulse dynamics of solitons with complex spectral and temporal features using deep learning due to the difficulty of capturing elaborated features in spectral and temporal domains. Herein, we propose and demonstrate a recurrent neural network with bidirectional long short-term memory (Bi-LSTMnet) to precisely predict full-field soliton dynamics in the MLFL. The inferring of the Bi-LSTMnet for 600 round trips of laser is less than 0.1 s, where the result is benchmarked with the ones obtained by the split-step Fourier method (SSFM). Typical prediction results of solitons with high-order Kelly sidebands and soliton molecules with complicated spectral and temporal features consolidate that the Bi-LSTMnet excels in accurately predicting the full-field evolutions of soliton dynamics and generalization capacity for forecasting evolution beyond the training dataset. Our results might facilitate efficient design and optimization of MLFLs.
Source: Optics & Laser Technology
Authors: Lin Gu, Tuqiang Pan, Yi Xu, Di Lin, Songnian Fu, Yuwen Qin
Institution: Guangdong University of Technology
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
OpenAlex ID: W4404023713