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

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

Radial Basis Function (RBF) methods have become the primary tool for interpolating
multidimensional scattered data. RBF methods also have become important tools for solving …

This application-oriented work concerns the design of efficient, robust and reliable
algorithms for the numerical simulation of multiscale phenomena. To this end, various …

We construct a new adaptive algorithm for radial basis functions (RBFs) method applied to
interpolation, boundary-value, and initial-boundary-value problems with localized features …

We present a novel hyperviscosity formulation for stabilizing RBF-FD discretizations of the
advection–diffusion equation. The amount of hyperviscosity is determined quasi-analytically …

We present a generalization of the RBF-FD method that computes RBF-FD weights in finite-
sized neighborhoods around the centers of RBF-FD stencils by introducing an overlap …

The aim of the BENCHOP project is to provide the finance community with a common suite
of benchmark problems for option pricing. We provide a detailed description of the six …

In this paper we propose an enhanced version of the residual sub-sampling method (RSM)
in Driscoll and Heryudono (2007) for adaptive interpolation by radial basis functions (RBFs) …

A conservative semi‐Lagrangian method is designed in order to solve linear advection
equations in two space variables. The advection scheme works with finite volumes on an …