A Physics-Informed Neural Network for Higher-Order Soliton Compression in Fibers

被引：0

作者：

Wu, Jinhong ^{[1
]}

Wang, Zimiao ^{[1
]}

Li, Qian ^{[1
]}

机构：

[1] Peking Univ, Sch Elect & Comp Engn, Shenzhen 518055, Peoples R China

来源：

2022 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE, ACP | 2022年

关键词：

PINN; higher-order soliton compression;

D O I：

10.1109/ACP55869.2022.10088889

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We construct a physics-informed neural network to predict the higher-order soliton compression. Through embedding a partial differential equation into the neural network, the evolution of soliton in nonlinear fiber can be predicted accurately and efficiently.

引用

页码：71 / 73

页数：3

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