The spectral analysis and data products in Data Release 16 (DR16; 2019 December) from
the high-resolution near-infrared Apache Point Observatory Galactic Evolution Experiment …

Implicit representations can provide simple descriptions of relatively complex shapes and
can in many cases be a good choice when designing stable shape processing algorithms …

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 …

We consider a preconditioned Krylov subspace iterative algorithm presented by Faul,
Goodsell, and Powell (IMA J. Numer. Anal. 25 (2005), pp. 1–24) for computing the …

Vortex methods have a history as old as finite differences. They have since faced difficulties
stemming from the numerical complexity of the Biot–Savart law, the inconvenience of adding …

We have developed a parallel algorithm for radial basis function (rbf) interpolation that
exhibits O (N) complexity, requires O (N) storage, and scales excellently up to a thousand …

Gaussian processes are the cornerstone of statistical analysis in many application areas.
Nevertheless, most of the applications are limited by their need to use the Cholesky …

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 …

Meshfree radial basis function (RBF) methods are of interest for solving partial differential
equations due to attractive convergence properties, flexibility with respect to geometry, and …