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Data · dataset · 2026

Stable computations with Gaussian radial basis functions

Listed in National Center for Atmospheric Research

Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in nontrivial geometries since the method is mesh-free and can be spectrally accurate.

Description

A perceived practical obstacle is that the interpolation matrix becomes increasingly ill-conditioned as the RBF shape parameter becomes small, corresponding to flat RBFs. Two stable approaches that overcome this problem exist: the Contour-Padé method and the RBF-QR method.

However, the former is limited to small node sets, and the latter has until now been formulated only for the surface of the sphere. This paper focuses on an RBF-QR formulation for node sets in one, two, and three dimensions. The algorithm is stable for arbitrarily small shape parameters.

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It can be used for thousands of node points in two dimensions and still more in three dimensions. A sample MATLAB code for the two-dimensional case is provided.

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National Center for Atmospheric Research0cb335b3-8a22-4a1a-a69c-a70663af0b4c12 d agoJSON v1
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concepts[field].local:field:ocean-atmosphericmapping · data ucar educonnector:data_ucar_edu@1.0.0
created_datesource · data ucar educonnector:data_ucar_edu@1.0.0
descriptionsource · data ucar educonnector:data_ucar_edu@1.0.0/notes
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titlesource · data ucar educonnector:data_ucar_edu@1.0.0/title
updated_datesource · data ucar educonnector:data_ucar_edu@1.0.0