Soliton Molecule Dynamics Evolution Prediction Based on <i>LSTM</i> Neural Networks

He, Jiangyong; Li, Caiyun; Wang, Pan; Liu, Congcong; Liu, Yange; Liu, Bo; Xing, Dengke; Wangc, Zhi

doi:10.1109/lpt.2022.3143127

Cited by 10 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, soliton collisions [5,6] , soliton molecules [7,8] , and soliton explosions [9,10] have been extensively studied, both experimentally and theoretically. In recent years, as a universal modeling scheme of complex systems, deep learning has been widely used in the field of nonlinear dynamics, such as predicting pulse propagation dynamics [11,12] , characterizing ultrashort optical pulses [13] , predicting the dynamics in PMLFLs [14] , and modeling physically analytic soliton interactions [15,16] .…”

Section: Introductionmentioning

confidence: 99%

Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network

Liu

Wang

et al. 2023

Chin. Opt. Lett.

View full text Add to dashboard Cite

In this article, we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber laser. Based on the particle characteristic in double solitons and triple solitons interactions, we found that there is a strict correspondence between the number of minimum compression parameters and the number of independent parameters of soliton interaction. This shows that our network effectively coarsens the high-dimensional data in nonlinear systems. Our work not only introduces new prospects for the laser self-optimization algorithm, but also brings new insights into the modeling of nonlinear systems and description of soliton interactions.

show abstract

Section: Introductionmentioning

confidence: 99%

Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network

Liu

Wang

et al. 2023

Chin. Opt. Lett.

View full text Add to dashboard Cite

show abstract

Artificial Intelligence-Enabled Mode-Locked Fiber Laser: A Review

Ma,

2023

Nanomanuf Metrol

View full text Add to dashboard Cite

Owing to their compactness, robustness, low cost, high stability, and diffraction-limited beam quality, mode-locked fiber lasers play an indispensable role in micro/nanomanufacturing, precision metrology, laser spectroscopy, LiDAR, biomedical imaging, optical communication, and soliton physics. Mode-locked fiber lasers are a highly complex nonlinear optical system, and understanding the underlying physical mechanisms or the flexible manipulation of ultrafast laser output is challenging. The traditional research paradigm often relies on known physical models, sophisticated numerical calculations, and exploratory experimental attempts. However, when dealing with several complex issues, these traditional approaches often face limitations and struggles in finding effective solutions. As an emerging data-driven analysis and processing technology, artificial intelligence (AI) has brought new insights into the development of mode-locked fiber lasers. This review highlights the areas where AI exhibits potential in accelerating the development of mode-locked fiber lasers, including nonlinear dynamics prediction, ultrashort pulse characterization, inverse design, and automatic control of mode-locked fiber lasers. Furthermore, the challenges and potential future development are discussed.

show abstract