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Power evolution prediction of bidirectional Raman amplified WDM system based on PINN

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成果类型:
期刊论文
作者:
Mei, Muyang;Li, Yuan;Niu, Mengchao;Zhu, Jiaye;Li, Wei;...
通讯作者:
Li, W
作者机构:
[Li, Wei; Mei, Muyang; Feng, Zhongshuai; Li, W] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China.
[Niu, Mengchao; Zhu, Jiaye; Li, Yuan] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Luo, Ming] China Informat & Commun Technol Grp Corp, State Key Lab Opt Commun Technol & Networks, Wuhan 430205, Peoples R China.
[Mei, Liang; Wu, Xuefeng] Fiberhome Telecommun Technol Co Ltd, Wuhan 430205, Peoples R China.
[Yang, Xuefeng; Jiang, Yi; Hu, Qianggao] Accelink Technol Co Ltd, Wuhan 430205, Peoples R China.
通讯机构:
[Li, W ] H
Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China.
语种:
英文
关键词:
Numerical methods;Data driven;Large amounts of data;Learn+;Machine-learning;Model-based OPC;Multiplexed systems;Neural-networks;Power;Rayleigh backscattering;Wave length division multiplexed (WDM);Forecasting;adult;article;clinical article;controlled study;evolution;machine learning;nerve cell network;noise;pharmaceutics;prediction;prediction error;simulation
期刊:
Optics Express
ISSN:
1094-4087
年:
2024
卷:
32
期:
4
页码:
6587-6596
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
We propose using physical-informed neural network (PINN) for power evolution prediction in bidirectional Raman amplified WDM systems with Rayleigh backscattering (RBS). Unlike models based on data-driven machine learning, PINN can be effectively trained without preparing a large amount of data in advance and can learn the potential rules of power evolution. Compared to previous applications of PINN in power prediction, our model considers bidirectional Raman pumping and RBS, which is more practical. We experimentally demonstrate power evolution...

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