Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method (Q2107164)

scientific article; zbMATH DE number 7625595

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English	Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method	scientific article; zbMATH DE number 7625595

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instance of

scholarly article

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title

Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method (English)

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author

Yanyan Li

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publication date

1 December 2022

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zbMATH Keywords

higher-order nonlinear Schrödinger equation

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strongly-constrained physics-informed neural network

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soliton dynamics

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soliton solution

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describes a project that uses

XPINNs

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MaRDI profile type

Publication

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full work available at URL

https://doi.org/10.1016/j.camwa.2022.09.025

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cites work

Well-posedness and asymptotic behavior of a generalized higher order nonlinear Schrödinger equation with localized dissipation

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Fourth-order compact and energy conservative difference schemes for the nonlinear Schrödinger equation in two dimensions

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Analytical solutions for the coupled Hirota equations in the firebringent fiber

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Focusing and defocusing Hirota equations with non-zero boundary conditions: inverse scattering transforms and soliton solutions

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Darboux transformation and explicit solutions for the Satsuma-Hirota coupled equation

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Parallel physics-informed neural networks via domain decomposition

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An efficient neural network method with plane wave activation functions for solving Helmholtz equation

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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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PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

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Physics-informed neural networks for inverse problems in supersonic flows

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Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves

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Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint

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When and why PINNs fail to train: a neural tangent kernel perspective

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When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

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A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code

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Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

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An Inter-modulated Solitary Wave Solution for the Higher Order Nonlinear Schrödinger Equation

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published in

Computers & Mathematics with Applications

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CAMWA.2022.09.025

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Sitelinks

Mathematics(1 entry)

mardi Publication:2107164