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Published in:
2020 | OriginalPaper | Chapter
Adaptive Refinement Techniques for RBF-PU Collocation
Authors : R. Cavoretto, A. De Rossi
Published in: Large-Scale Scientific Computing
Publisher: Springer International Publishing
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Abstract
We propose new adaptive refinement techniques for solving Poisson problems via a collocation radial basis function partition of unity (RBF-PU) method. As the construction of an adaptive RBF-PU method is still an open problem, we present two algorithms based on different error indicators and refinement strategies that turn out to be particularly suited for a RBF-PU scheme. More precisely, the first algorithm is characterized by an error estimator based on the comparison of two collocation solutions evaluated on a coarser set and a finer one, while the second one depends on an error estimate that is obtained by a comparison between the global collocation solution and the associated local RBF interpolant. Numerical results support our study and show the effectiveness of our algorithms.