Training RBF neural networks for the solution of elliptic boundary value problems
DOI10.1016/j.camwa.2022.08.029OpenAlexW4301180141MaRDI QIDQ2094353
Andreas Karageorghis, Ching-Shyang Chen
Publication date: 28 October 2022
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.camwa.2022.08.029
neural networksradial basis functionscollocationelliptic boundary value problemsnonlinear minimization
Learning and adaptive systems in artificial intelligence (68T05) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Neural networks for/in biological studies, artificial life and related topics (92B20) Numerical interpolation (65D05) Numerical radial basis function approximation (65D12)
Uses Software
Cites Work
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- A random variable shape parameter strategy for radial basis function approximation methods
- Efficient solving of boundary value problems using radial basis function networks learned by trust region method
- Multiquadrics -- a scattered data approximation scheme with applications to computational fluid-dynamics. II: Solutions to parabolic, hyperbolic and elliptic partial differential equations
- Solving biharmonic problems with scattered-point discretization using indirect radial-basis-function networks
- Results on meshless collocation techniques
- A local radial basis function collocation method for band structure computation of 3D phononic crystals
- Learning radial basis function networks with the trust region method for boundary problems
- The Kansa RBF method with auxiliary boundary centres for fourth order boundary value problems
- Kansa radial basis function method with fictitious centres for solving nonlinear boundary value problems
- A time-efficient variable shape parameter Kansa-radial basis function method for the solution of nonlinear boundary value problems
- An introduction to neural network methods for differential equations
- Variable shape parameter Kansa RBF method for the solution of nonlinear boundary value problems
- A novel RBF collocation method using fictitious centres
- Solving boundary value problems of mathematical physics using radial basis function networks
- Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey
- The method of approximate particular solutions for solving certain partial differential equations
- Unified approach to constructing the neural network models of real objects. Part 1
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