Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN

Abstract

We propose the two-subnet physical information neural network with the weighted loss function (WL-tsPINN) to study the higher-order effects of ultra-short pulses in birefringence fiber transmission and analyze the formation mechanism of vector solitons. We predict the dynamical process of mixed-type single/double soliton and soliton molecules based on the higher-order coupled nonlinear Schrödinger equation (CNLSE) by this WL-tsPINN method. Moreover, we deduce the physical coefficients of the higher-order CNLSE from the mixed single soliton solution. Deep learning based on neural network is a powerful tool for further study of higher-order CNLSE and has potential significance for further study of soliton dynamics.