2016
DOI: 10.1016/j.jcp.2016.05.026
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On the role of polynomials in RBF-FD approximations: I. Interpolation and accuracy

Abstract: Radial basis function-generated finite difference (RBF-FD) approximations generalize classical grid-based finite differences (FD) from lattice-based to scattered node layouts. This greatly increases the geometric flexibility of the discretizations and makes it easier to carry out local refinement in critical areas. Many different types of radial functions have been considered in this RBF-FD context. In this study, we find that (i) polyharmonic splines (PHS) in conjunction with supplementary polynomials provide… Show more

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Cited by 235 publications
(276 citation statements)
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“…Depending on the application, it may be interesting to consider interpolants in which the degree of the polynomial part is arbitrarily high. It has been shown in [11] that higher accuracy can be achieved for problems on circular domains by introducing supplementary polynomials. It is also observed that small shape parameters can then be used even without employing stable algorithms.…”
Section: Infinitely Smooth Rbfsmentioning
confidence: 99%
“…Depending on the application, it may be interesting to consider interpolants in which the degree of the polynomial part is arbitrarily high. It has been shown in [11] that higher accuracy can be achieved for problems on circular domains by introducing supplementary polynomials. It is also observed that small shape parameters can then be used even without employing stable algorithms.…”
Section: Infinitely Smooth Rbfsmentioning
confidence: 99%
“…This results in a less oscillatory interpolant and thus more accurate derivative approximations. Further augmentation with more polynomials is currently being studied in Flyer et al (2015a) and Bayona et al (2015). As a result, the system of equations that determines the RBF-FD differentiation weight w i to approximate Lu is…”
Section: Rbf-fd Methodsmentioning
confidence: 99%
“…We can rewrite (14) so that it explicitly depends only on the vector of samples f S1 using (9) as follows: (15) (…”
Section: Computation Of the Weights From Iterated Differentiation Thmentioning
confidence: 99%
“…There are workarounds for both of these problems. In the stationary interpolation case, it is possible to recover high-order convergence by adding suitable polynomial terms to the interpolant [14,3]. On a surface, however, the polynomials may themselves introduce ill-conditioning, especially if it is an algebraic surface as polynomial unisolvency becomes an issue.…”
Section: Parameter Studiesmentioning
confidence: 99%