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Data-driven prediction of spatial optical solitons in fractional diffraction

Author

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

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.

Suggested Citation

  • Fang, Yin & Zhu, Bo-Wei & Bo, Wen-Bo & Wang, Yue-Yue & Dai, Chao-Qing, 2023. "Data-driven prediction of spatial optical solitons in fractional diffraction," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p2:s0960077923009864
    DOI: 10.1016/j.chaos.2023.114085
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    References listed on IDEAS

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    1. Fang, Yin & Wu, Gang-Zhou & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Wu, Gang-Zhou & Fang, Yin & Wang, Yue-Yue & Wu, Guo-Cheng & Dai, Chao-Qing, 2021. "Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Fang, Yin & Bo, Wen-Bo & Wang, Ru-Ru & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Pu, Jun-Cai & Chen, Yong, 2022. "Data-driven vector localized waves and parameters discovery for Manakov system using deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    Full references (including those not matched with items on IDEAS)

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