The Matlab radial basis function toolbox. Radial Basis Function (RBF) methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Campagna, R.; Cuomo, S.; De Marchi, S.; Perracchione, E.; Severino, G.: A stable meshfree PDE solver for source-type flows in porous media (2020)
- De Marchi, S.; Martínez, A.; Perracchione, E.; Rossini, M.: RBF-based partition of unity methods for elliptic PDEs: adaptivity and stability issues via variably scaled kernels (2019)
- Gilani, Faheem; Harlim, John: Approximating solutions of linear elliptic PDE’s on a smooth manifold using local kernel (2019)
- Sarra, Scott A.: Radial basis function methods -- reduced computational expense by exploiting symmetry (2018)
- Sarra, Scott A.; Bai, Yikun: A rational radial basis function method for accurately resolving discontinuities and steep gradients (2018)
- Sarra, S.A.: The Matlab Radial Basis Function Toolbox (2017) not zbMATH
- Yensiri, Suranon; Skulkhu, Ruth J.: An investigation of radial basis function-finite difference (RBF-FD) method for numerical solution of elliptic partial differential equations (2017)