Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN. (November 2021)

• Record Type: Journal Article
• Authors: Wu, Gang-Zhou; Fang, Yin; Wang, Yue-Yue; Wu, Guo-Cheng; Dai, Chao-Qing
• Is Part Of: Chaos, solitons and fractals. Volume 152(2021)
• Publication Date: 2021-11
• Subjects: Modified physics-informed neural network -- Coupled nonlinear Schrödinger equation -- Vector optical solitons -- Dispersion coefficients and nonlinearity coefficients; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.111393
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: The PINN method is improved to solve coupled PDEs, such as the coupled NLSE. The PINN method is firstly used to predict the dynamic process of dark soliton. The network is optimized by the data sets and the proportion of the loss function. 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 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.