Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the \((2+1)\)-dimensional CHKP equation (Q6554449)

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scientific article; zbMATH DE number 7864211

Language	Label	Description	Also known as
English	Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the \((2+1)\)-dimensional CHKP equation	scientific article; zbMATH DE number 7864211

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scholarly article

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Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the \((2+1)\)-dimensional CHKP equation (English)

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publication date

12 June 2024

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MaRDI profile type

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PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background

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1-soliton solution of the generalized Camassa-Holm Kadomtsev-Petviashvili equation

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The Camassa-Holm-KP equations with compact and noncompact travelling wave solutions

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On breather waves, rogue waves and solitary waves to a generalized (2+1)-dimensional Camassa-Holm-Kadomtsev-Petviashvili equation

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Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning

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Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method

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On the limited memory BFGS method for large scale optimization

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Multiple rogue wave solutions for the generalized (2+1)-dimensional Camassa-Holm-Kadomtsev-Petviashvili equation

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Identifiers

zbMATH Open document ID

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10.1063/5.0193513

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:6554449

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