Principle-driven Fiber Transmission Model based on PINN Neural Network

• Main Authors: Zang, Yubin, Yu, Zhenming, Xu, Kun, Lan, Xingzeng, Chen, Minghua, Yang, Sigang, Chen, Hongwei
• Format: Journal Article
• Language: English
• Published: 24-08-2021
• DOI: 10.48550/arxiv.2108.10734

In this paper, a novel principle-driven fiber transmission model based on
physical induced neural network (PINN) is proposed. Unlike data-driven models
which regard fiber transmission problem as data regression tasks, this model
views it as an equation solving problem. Instead of adopting input signals and
output signals which are calculated by SSFM algorithm in advance before
training, this principle-driven PINN based fiber model adopts frames of time
and distance as its inputs and the corresponding real and imaginary parts of
NLSE solutions as its outputs. By taking into account of pulses and signals
before transmission as initial conditions and fiber physical principles as NLSE
in the design of loss functions, this model will progressively learn the
transmission rules. Therefore, it can be effectively trained without the data
labels, referred as the pre-calculated signals after transmission in
data-driven models. Due to this advantage, SSFM algorithm is no longer needed
before the training of principle-driven fiber model which can save considerable
time consumption. Through numerical demonstration, the results show that this
principle-driven PINN based fiber model can handle the prediction tasks of
pulse evolution, signal transmission and fiber birefringence for different
transmission parameters of fiber telecommunications.