Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

We survey some recent applications of radial basis functions (rbfs) for the BEM and related
algorithms such as the method of fundamental solutions. Among these are the use of …

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 …

Many practical applications require the reconstruction of a multivariate function from
discrete, unstructured data. This book gives a self-contained, complete introduction into this …

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 …

Supplement to “Strictly and non-strictly positive definite functions on spheres”. Appendix A
states and proves further criteria of Pólya type, thereby complementing Section 4.2 …

This textbook is designed for graduate students in mathematics, physics, engineering, and
computer science. Its purpose is to guide the reader in exploring contemporary …

Kernels are valuable tools in various fields of numerical analysis, including approximation,
interpolation, meshless methods for solving partial differential equations, neural networks …

This contribution gives a partial survey over the native spaces associated to (not necessarily
radial) basis functions. Starting from reproducing kernel Hilbert spaces and invariance …

The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …