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 Schrödinger 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.

基于双折射光纤中的耦合非线性薛定谔方程，使用改进的物理信息神经网络来预测光脉冲的动力学，包括单孤子、双孤子和流氓波。同时预测了明暗混合孤子的弹性碰撞过程。将预测结果与精确解进行比较，证明改进的物理信息神经网络方法可以有效地求解耦合非线性薛定谔方程。此外，耦合非线性薛定谔方程的色散系数和非线性系数可以通过改进的物理信息神经网络学习。这为我们利用深度学习方法研究光纤中孤子的动态特性提供了参考。