Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000)

scientific article; zbMATH DE number 7899950

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English	Machine learning for nonlinear integro-differential equations with degenerate kernel scheme	scientific article; zbMATH DE number 7899950

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instance of

scholarly article

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title

Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (English)

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Communications in Nonlinear Science and Numerical Simulation

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publication date

21 August 2024

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zbMATH Keywords

machine learning

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nonlinear integral-differential equations (IDEs)

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degenerate kernel scheme

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forward and inverse problem

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MaRDI profile type

Publication

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cites work

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Human-level concept learning through probabilistic program induction

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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

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Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning

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

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Adversarial uncertainty quantification in physics-informed neural networks

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Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equations

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Global Asymptotic Stability for a Stationary Solution of a System of Integro-Differential Equations Describing the Formation of Liver Zones

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Q4787287

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Homotopy Analysis Method in Nonlinear Differential Equations

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Usage of the homotopy analysis method for solving the nonlinear and linear integral equations of the second kind

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A Wavelet Numerical Method for Solving Nonlinear Fractional Vibration, Diffusion and Wave Equations

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Q2964850

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Utilizing feed-back neural network approach for solving linear Fredholm integral equations system

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Numerical solution of the second kind integral equations using radial basis function networks

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Fractional Chebyshev deep neural network (FCDNN) for solving differential models

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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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nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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fPINNs: Fractional Physics-Informed Neural Networks

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A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

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Direct computation method for solving a general nonlinear Fredholm integro-differential equation under the mixed conditions: degenerate and non-degenerate kernels

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CNSNS.2024.108242

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Sitelinks

Mathematics(1 entry)

mardi Publication:6591000