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

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 Schrödinger 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 Schrödinger 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.

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• Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN
• Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method