Abstract. Radial basis functions (RBF) is a recent methodology for scattered data interpolation, offering possibilities for meshfree numerical solution of PDEs to spectral accuracy also on irregular domains in any number of dimensions. We briefly introduce RBFs, and then focus on some recent developments, especially with regard to the Gibbs and the Runge phenomena for RBF interpolants. The latter suggests ways to further enhance the RBF methodology for solving PDEs
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods wh...
Interpolation or approximation of scattered data is very often task in engineering problems. The Rad...
In recent years radial basis function collocation has become a useful alternative to finite differe...
Radial Basis Function (RBF) methods have become a truly meshless alternative for the interpolation o...
Radial Basis Function (RBF) methods have become a truly meshless alternative for the interpolation o...
Radial basis functions (RBFs) are constructed in terms of one-dimensional distance variable and appe...
The traditional basis functions in numerical PDEs are mostly coordinate functions, such as polynomia...
Radial basis function (RBF) methods are meshfree, i.e., they can operate on unstructured node sets. ...
Radial basis function (RBF) methods are meshfree, i.e., they can operate on unstructured node sets. ...
Radial Basis Functions (RBFs) are a powerful numerical methodology for solving PDEs to high accuracy...
Abstract. What is now known as the Gibbs phenomenon was first observed in the context of truncated F...
Radial Basis Functions (RBF) interpolation theory is briefly introduced at the “application level” i...
Abstract. Radial basis functions (RBFs) form a primary tool for multivariate interpolation, and they...
AbstractRadial basis functions (RBFs) form a primary tool for multivariate interpolation, and they a...
Abstract. We explore a connection between Gaussian radial basis functions and polynomials. Using sta...
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods wh...
Interpolation or approximation of scattered data is very often task in engineering problems. The Rad...
In recent years radial basis function collocation has become a useful alternative to finite differe...
Radial Basis Function (RBF) methods have become a truly meshless alternative for the interpolation o...
Radial Basis Function (RBF) methods have become a truly meshless alternative for the interpolation o...
Radial basis functions (RBFs) are constructed in terms of one-dimensional distance variable and appe...
The traditional basis functions in numerical PDEs are mostly coordinate functions, such as polynomia...
Radial basis function (RBF) methods are meshfree, i.e., they can operate on unstructured node sets. ...
Radial basis function (RBF) methods are meshfree, i.e., they can operate on unstructured node sets. ...
Radial Basis Functions (RBFs) are a powerful numerical methodology for solving PDEs to high accuracy...
Abstract. What is now known as the Gibbs phenomenon was first observed in the context of truncated F...
Radial Basis Functions (RBF) interpolation theory is briefly introduced at the “application level” i...
Abstract. Radial basis functions (RBFs) form a primary tool for multivariate interpolation, and they...
AbstractRadial basis functions (RBFs) form a primary tool for multivariate interpolation, and they a...
Abstract. We explore a connection between Gaussian radial basis functions and polynomials. Using sta...
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods wh...
Interpolation or approximation of scattered data is very often task in engineering problems. The Rad...
In recent years radial basis function collocation has become a useful alternative to finite differe...