Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers
arXiv:2407.18725 · doi:10.1002/lpor.202400097
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.
To be published in Laser & Photonics Reviews;https://doi.org/10.1002/lpor.202400097
References in corpus (9)
- Learning data driven discretizations for partial differential equations
- End-to-end Deep Learning of Optical Fiber Communications
- Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons
- Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network
- Farey tree and devil's staircase of frequency-locked breathers in ultrafast lasers
- Path sampling of recurrent neural networks by incorporating known physics
- Flipping-shuttle oscillations of bright one- and two-dimensional solitons in spin-orbit-coupled Bose-Einstein condensates with Rabi mixing
- Bayesian Optimization of Bose-Einstein Condensates
- Soliton oscillations in the Zakharov-type system at arbitrary nonlinearity-dispersion ratio