Radial basis function methods are modern ways to approximate multivariate functions,
especially in the absence of grid data. They have been known, tested and analysed for …

Meshfree approximation methods are a relatively new area of research, and there are only a
few books covering it at present. Whereas other works focus almost entirely on theoretical …

This book is focused on a powerful numerical methodology for solving PDEs to high
accuracy in any number of dimensions: Radial Basis Functions (RBFs). During the past …

Radial basis function networks (RBFNs) have gained widespread appeal amongst
researchers and have shown good performance in a variety of application domains. They …

Meshfree methods are the topic of recent research in many areas of computational science
and approximation theory. These methods come in various flavors, most of which can be …

Although meshless radial basis function (RBF) methods applied to partial differential
equations (PDEs) are not only simple to implement and enjoy exponential convergence …

A review of interpolation to values of a function f (x), x 2 Rd, by radial basis function methods
is given. It addresses the nonsingularity of the interpolation equations, the inclusion of …

This is “my” part of a future book “Scientific Computing with Radial Basis Functions” I am
currently writig with my colleagues CS Chen and YC Hon. I took a preliminary version out of …

A space–time functional form for some contaminants is obtained and used for estimating
total air pollution (TAP) in the district of Milan, Italy, during selected high-risk days of 1999 …

Scientific Computing with Radial Basis Functions Page 1 Scientific Computing with Radial Basis
Functions Edited by CS Chen University of Southern Mississippi, USA YC Hon City University …