Efficient solving of boundary value problems using radial basis function networks learned by trust region method

DOI10.1155/2018/9457578zbMath1486.65185OpenAlexW2805476668MaRDI QIDQ1652941

Mustafa Sadeq Jaafar, Vladimir Ivanovich Gorbachenko, Mohie Mortadha Alqezweeni, Maxim Valerievich Zhukov

Publication date: 17 July 2018

Published in: International Journal of Mathematics and Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/9457578

zbMATH Keywords

boundary value problemsradial basis function networks

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Numerical interpolation (65D05) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70)

Related Items (3)

Unnamed Item ⋮ Random vibration of hysteretic systems under Poisson white noise excitations ⋮ Training RBF neural networks for the solution of elliptic boundary value problems

Uses Software

• Matlab
• Mfree2D

Cites Work

• Unnamed Item
• Space-fractional advection-dispersion equations by the Kansa method
• A fast semi-discrete Kansa method to solve the two-dimensional spatiotemporal fractional diffusion equation
• An introduction to neural network methods for differential equations
• Solving boundary value problems of mathematical physics using radial basis function networks
• The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
• Radial Basis Functions
• Trust Region Methods

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