Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264)

scientific article; zbMATH DE number 7882862

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English	Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method	scientific article; zbMATH DE number 7882862

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instance of

scholarly article

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title

Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (English)

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Communications in Applied Mathematics and Computational Science

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publication date

18 July 2024

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zbMATH Keywords

AB system

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physics informed neural network

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rogue wave

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phase transition

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Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

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Error estimates for physics-informed neural networks approximating the Navier-Stokes equations

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Q3961178

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An example of soliton behaviour in a rotating baroclinic fluid

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Reducing the Dimensionality of Data with Neural Networks

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

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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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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Periodic solutions and Whitham equations for the AB system

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions

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The finite difference method at arbitrary irregular grids and its application in applied mechanics

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Physics-informed neural networks for high-speed flows

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Self-adaptive physics-informed neural networks

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Multi-fidelity Bayesian neural networks: algorithms and applications

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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Finite-amplitude baroclinic instability of a mesoscale gravity current in a channel

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Evolution of baroclinic wave packets in a flow with continuous shear and stratification

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

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fPINNs: Fractional Physics-Informed Neural Networks

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Meta-learning PINN loss functions

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Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

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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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Parallel physics-informed neural networks via domain decomposition

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DGM: a deep learning algorithm for solving partial differential equations

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Envelope Solitary Waves and Periodic Waves in the AB Equations

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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A coupled “<i>AB</i>” system: Rogue waves and modulation instabilities

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Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN

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Bright-dark soliton, breather and semirational rogue wave solutions for a coupled AB system

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Bright-dark soliton solutions of the multi-component AB system

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Adversarial uncertainty quantification in physics-informed neural networks

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SLeNN-ELM: a shifted Legendre neural network method for fractional delay differential equations based on extreme learning machine

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

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Weak adversarial networks for high-dimensional partial differential equations

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Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

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

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Wave-packet behaviors of the defocusing nonlinear Schrödinger equation based on the modified physics-informed neural networks

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Physics-informed semantic inpainting: application to geostatistical modeling

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Algorithm 778: L-BFGS-B

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Identifiers

zbMATH Open document ID

1545.65479

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Mathematics Subject Classification ID

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10.2140/CAMCOS.2024.19.1

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Sitelinks

Mathematics(1 entry)

mardi Publication:6574264