Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones
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DOI: 10.1016/j.chaos.2023.114090
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- 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).
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- 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).
- Zhong, Ming & Yan, Zhenya, 2022. "Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
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Cited by:
- Li, Wentao & Li, Biao, 2024. "Construction of degenerate lump solutions for (2+1)-dimensional Yu-Toda-Sasa-Fukuyama equation," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
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Keywords
Deep learning; XPINNs algorithm with interface zones; Massive Thirring model; Data-driven localized waves; Parameter discovery;All these keywords.
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