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Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN

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

Abstract

The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed neural network (PINN). It is used to predict nonlinear dynamics and the formation process of bright and dark picosecond optical solitons, and femtosecond soliton molecule in the single-mode fiber, and reveal the variation of physical quantities including the energy, amplitude, spectrum and phase of pulses during the soliton transmission. The adaptive weight is introduced to accelerate the convergence of loss function in this new neural network. Compared with the PINN, the accuracy of SCPINN in predicting soliton dynamics is improved by 5–11 times. Therefore, the SCPINN is a forward-looking method to study the modeling and analysis of soliton dynamics in the fiber.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p1:s0960077922010876
    DOI: 10.1016/j.chaos.2022.112908
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    References listed on IDEAS

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    Cited by:

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    4. Qin, Chun-Yan & Zhang, Run-Fa & Li, Yao-Hong, 2024. "Various exact solutions of the (4+1)-dimensional Boiti–Leon–Manna–Pempinelli-like equation by using bilinear neural network method," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    5. Chen, Liang-Yuan & Wu, Hong-Yu & Jiang, Li-Hong, 2024. "Ring-like two-breather structures of a partially nonlocal NLS system with different two-directional diffractions under a parabolic potential," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    6. 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).
    7. Yin, Yu-Hang & Lü, Xing, 2024. "Multi-parallelized PINNs for the inverse problem study of NLS typed equations in optical fiber communications: Discovery on diverse high-order terms and variable coefficients," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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