Data-driven prediction of spatial optical solitons in fractional diffraction

Yin Fang, Bo-Wei Zhu, Wen-Bo Bo, Yue-Yue Wang and Chao-Qing Dai

Chaos, Solitons & Fractals, 2023, vol. 175, issue P2

Abstract: A quasi-residual physics-informed neural network (QR_PINN) with efficient residual-like blocks, was investigated based on classical physics-informed neural network to solve nonlinear fractional Schrödinger equation and analyze the transmission of spatial optical solitons in saturable nonlinear media with fractional diffraction. A comprehensive verification of stable transmission of various solitons under PT-symmetric potential was carried out using the QR_PINN. In addition, the transmission of spatial optical solitons was studied under simple real potential (stable transmission) and complex Scarf-II potential (unstable transmission). The results show that the QR_PINN can accurately reconstruct the transmission of spatial optical solitons under fractional diffraction. Meanwhile, as the complexity of the potential function increases, the prediction accuracy of the QR_PINN slightly decreases. These results provide a new approach for the application of deep learning in the nonlinear fractional Schrödinger equation.

Keywords: Spatial optical solitons; Soliton dynamics; Quasi-residual physics-informed neural network; Nonlinear fractional Schrödinger equation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:175:y:2023:i:p2:s0960077923009864

DOI: 10.1016/j.chaos.2023.114085

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