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The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach - MaRDI portal

The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach (Q6558868)

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scientific article; zbMATH DE number 7868602

Language	Label	Description	Also known as
English	The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach	scientific article; zbMATH DE number 7868602

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scholarly article

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The data-driven rogue waves of the Hirota equation by using mix-training PINNs approach (English)

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publication date

21 June 2024

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zbMATH Keywords

physics-informed neural networks

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mixed training

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prior information

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Hirota equation

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rogue waves

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MaRDI profile type

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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fPINNs: Fractional Physics-Informed Neural Networks

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Physics-informed neural networks for inverse problems in supersonic flows

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

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Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations

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Solving second-order nonlinear evolution partial differential equations using deep learning

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A deep learning method for solving third-order nonlinear evolution equations

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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 discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes

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Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning

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A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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Characteristics of rogue waves on a periodic background for the Hirota equation

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Identifiers

Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.PHYSD.2024.134202

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Sitelinks

Mathematics(1 entry)

mardi Publication:6558868

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