Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN

Bo-Wei Zhu, Yin Fang, Wei Liu and Chao-Qing Dai

Chaos, Solitons & Fractals, 2022, vol. 162, issue C

Abstract: We propose the two-subnet physical information neural network with the weighted loss function (WL-tsPINN) to study the higher-order effects of ultra-short pulses in birefringence fiber transmission and analyze the formation mechanism of vector solitons. We predict the dynamical process of mixed-type single/double soliton and soliton molecules based on the higher-order coupled nonlinear Schrödinger equation (CNLSE) by this WL-tsPINN method. Moreover, we deduce the physical coefficients of the higher-order CNLSE from the mixed single soliton solution. Deep learning based on neural network is a powerful tool for further study of higher-order CNLSE and has potential significance for further study of soliton dynamics.

Keywords: Physical information neural network; Weighted loss function; Higher-order coupled nonlinear Schrödinger equation; Mixed-type vector optical solitons; Higher-order physical effect (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006518

DOI: 10.1016/j.chaos.2022.112441

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