Soliton Molecule Dynamics Evolution Prediction Based on LSTM Neural Networks

In this article, we design a long short-term memory ( LSTM ) scheme, combined with dense networks, to realize soliton dynamics prediction in passively mode-locked fiber lasers. Based on the particle characteristic of soliton interaction, we propose to use the separation and relative phase between so...

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Bibliographic Details
Published in:IEEE photonics technology letters 2022-02, Vol.34 (3), p.193-196
Main Authors: He, Jiangyong, Li, Caiyun, Wang, Pan, Liu, Congcong, Liu, Yange, Liu, Bo, Xing, Dengke, Wang, Zhi
Format: Article
Language:English
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">LSTM neural network
Algorithms
Collision dynamics
Dynamics
Fiber laser
Fiber lasers
Laser mode locking
Laser theory
Mathematical models
Neural networks
Nonlinear systems
Optical fibers
Optimization
Solitary waves
soliton
Solitons
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Summary:In this article, we design a long short-term memory ( LSTM ) scheme, combined with dense networks, to realize soliton dynamics prediction in passively mode-locked fiber lasers. Based on the particle characteristic of soliton interaction, we propose to use the separation and relative phase between solitons as characteristic parameters to model and predict the dynamics. The network predicted soliton collision and soliton molecule dynamics accurately. This scheme of precoding physical information with subsequent dynamics prediction not only introduces new prospects for the laser self-optimization algorithm, but also brings new insights for the modeling of nonlinear systems and description of soliton interactions.
ISSN:1041-1135
1941-0174
DOI:10.1109/LPT.2022.3143127