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arXiv · 2108.11956

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

Gang-Zhou Wu·Yin Fang·Yue-Yue Wang·Guo-Cheng Wu·Chao-Qing Dai
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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.

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Gang-Zhou Wu, Yin Fang, Yue-Yue Wang, Guo-Cheng Wu, Chao-Qing Dai. 2021-08-26. Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN. https://doi.org/10.1016/j.chaos.2021.111393

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