Principle-driven Fiber Transmission Model based on PINN Neural Network

TL;DR

This paper introduces a physics-informed neural network (PINN) model for fiber transmission that learns transmission rules directly from physical principles, eliminating the need for pre-calculated data and reducing computational time.

Contribution

It presents a novel principle-driven PINN approach for fiber transmission modeling that directly incorporates physical laws, improving efficiency and accuracy over data-driven methods.

Findings

Successfully predicts pulse evolution and signal transmission.

Reduces training time by avoiding pre-calculated data.

Handles various transmission parameters effectively.

Abstract

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.