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Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

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  • Wu, Gang-Zhou
  • Fang, Yin
  • Wang, Yue-Yue
  • Wu, Guo-Cheng
  • Dai, Chao-Qing

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.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921007475
    DOI: 10.1016/j.chaos.2021.111393
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    References listed on IDEAS

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    Cited by:

    1. Wu, Gang-Zhou & Fang, Yin & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Jaganathan, Meiyazhagan & Bakthavatchalam, Tamil Arasan & Vadivel, Murugesan & Murugan, Selvakumar & Balu, Gopinath & Sankarasubbu, Malaikannan & Ramaswamy, Radha & Sethuraman, Vijayalakshmi & Malomed, 2023. "Data-driven multi-valley dark solitons of multi-component Manakov Model using Physics-Informed Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Zhu, Bo-Wei & Fang, Yin & Liu, Wei & Dai, Chao-Qing, 2022. "Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    4. 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).
    5. 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).
    6. Cai, Yue-Jin & Wu, Jian-Wen & Lin, Ji, 2022. "Nondegenerate N-soliton solutions for Manakov system," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Zhang, Yabin & Wang, Lei & Zhang, Peng & Luo, Haotian & Shi, Wanlin & Wang, Xin, 2022. "The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    8. 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).
    9. 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).

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