Data-driven prediction of spatial optical solitons in fractional diffraction
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DOI: 10.1016/j.chaos.2023.114085
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- 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).
- 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).
- 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).
- 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).
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- Wu, Hong-Yu & Jiang, Li-Hong, 2024. "3D partially nonlocal ring-like Kuznetsov-Ma and Akhmediev breathers of NLS model with different diffractions under a linear potential," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
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Keywords
Spatial optical solitons; Soliton dynamics; Quasi-residual physics-informed neural network; Nonlinear fractional Schrödinger equation;All these keywords.
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