Radial basis functions: A class of grid-free, scattered data approximations

Abstract

Recent developments in the theory and applications of radial basis functions are summarized. One intriguing property of such approximation methods is that rates of convergence improve with increasing dimensions. Also, such approximations, under favorable circumstances, can have exponential convergence. Numerical applications, especially on sparse scattered data sets of high dimensions, validate these theoretical claims.