Aiton, K.W. and Driscoll, T.A. (2019) An Adaptive Partition of Unity Method for Multivariate Chebyshev Polynomial Approximations. SIAM Journal on Scientific Computing, 41, A3230-A3245.
https://doi.org/10.1137/18M1184904

has been cited by the following article:

• TITLE: Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

  AUTHORS: Scott A. Sarra

  KEYWORDS: Radial Basis Functions, Shape Parameter Selection, Quasi-Random Centers, Numerical PDEs, Scientific Computing, Open Source Software, Python Programming Language, Reproducible Research

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.12, December 25, 2023

  ABSTRACT: Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.