Modeling pulse propagation in fiber optical parametric amplifier by a long short-term memory network

Sui, Hao; Zhu, Hongna; Wu, Jianhui; Luo, Bin; Taccheo, S.; Zou, Xihua

doi:10.1016/j.ijleo.2022.169125

Cited by 5 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long short-term memory (LSTM), one type of recurrent neural network (RNN), is very popular for data prediction due to its ability to capture long-term dependencies, the handling of variable-length of sequences, model temporal lags, and the excellent tolerance with noise or irregular data [7][8][9][10] . Some groups focused on the prediction of the ultrafast nonlinear dynamics in fiber optics with machine learning method 7,8,11 . Herein, to better fill the gap for generating a universal framework for designing ultrafast pulse shaping procedures, we present a new framework for photoemission laser systems and dynamic laser shaping.…”

Section: Introductionmentioning

confidence: 99%

A new integrated machine learning framework for advanced photoemission

Zhang,

Hirschman,

Lemons

et al. 2023

Laser Beam Shaping XXIII

View full text Add to dashboard Cite

The next generation of ultra-bright photoemission sources may offer opportunities to enhance our understanding of fundamental spatiotemporal scales. However, modeling photoemission and laser shaping systems precisely and efficiently is difficult due to the numerous interdependent linear and nonlinear processes involved and significant variations in modeling frameworks. Here, we present a new machine learning-based framework for photoemission laser systems and dynamic laser shaping. To showcase the effectiveness of our approach in system optimization, reverse engineering, and design. Our framework is designed to facilitate precise adaptive temporal shaping, including the generation of longitudinally flat-top or periodically-modulated pulses, through integration with four-wave mixing.

show abstract