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 and Chao-Qing Dai

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

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.

Keywords: Modified physics-informed neural network; Coupled nonlinear Schrödinger equation; Vector optical solitons; Dispersion coefficients and nonlinearity coefficients (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921007475

DOI: 10.1016/j.chaos.2021.111393

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