Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

TL;DR

This paper employs a modified physics-informed neural network to accurately predict the dynamics and parameters of vector optical solitons in birefringent fibers, including complex interactions like rogue waves and soliton collisions.

Contribution

It introduces a modified PINN approach capable of predicting soliton dynamics and learning key parameters of the coupled nonlinear Schrödinger equation in optical fibers.

Findings

Effective prediction of soliton dynamics and rogue waves.

Successful learning of dispersion and nonlinearity coefficients.

Validation against exact solutions confirms method accuracy.

Abstract

A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schr\"odinger equation in birefringent fibers. At the same time, the elastic collision process of the mixed bright-dark soliton is predicted. Compared the predicted results with the exact solution, the modified physics-informed neural network method is proven to be effective to solve the coupled nonlinear Schr\"odinger equation. Moreover, the dispersion coefficients and nonlinearity coefficients of the coupled nonlinear Schrodinger equation can be learned by modified physics-informed neural network. This provides a reference for us to use deep learning methods to study the dynamic characteristics of solitons in optical fibers.