标题： 通过改进的PINN预测双折射光纤中矢量光孤子的动力学过程和模型参数 显示英文标题

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

摘要： 一种改进的物理信息神经网络被用来基于双折射光纤中的耦合非线性薛定谔方程预测光脉冲的动力学，包括单孤子、双孤子和 rogue 波。 同时，预测了混合亮暗孤子的弹性碰撞过程。 将预测结果与精确解进行比较，证明了改进的物理信息神经网络方法能够有效解决耦合非线性薛定谔方程。 此外，改进的物理信息神经网络可以学习耦合非线性薛定谔方程的色散系数和非线性系数。 这为我们使用深度学习方法研究光纤中孤子的动力学特性提供了参考。 显示英文摘要

摘要： 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.