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An Efficient Trivariate Algorithm for Tetrahedral Shepard Interpolation

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Abstract

In this paper we present a trivariate algorithm for fast computation of tetrahedral Shepard interpolants. Though the tetrahedral Shepard method achieves an approximation order better than classical Shepard formulas, it requires to detect suitable configurations of tetrahedra whose vertices are given by the set of data points. In doing that, we propose the use of a fast searching procedure based on the partitioning of domain and nodes in cubic blocks. This allows us to find the nearest neighbor points associated with each ball that need to be used in the 3D interpolation scheme. Numerical experiments show good performance of our interpolation algorithm.

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Acknowledgements

The authors sincerely thank the two anonymous referees for their insightful comments and suggestions, which gave the chance to significantly improve the quality of this paper. This work was partially supported by the INdAM-GNCS 2018 research project “Methods, algorithms and applications of multivariate approximation” and by the 2018 project “Mathematics for applications” funded by the Department of Mathematics “Giuseppe Peano” of the University of Turin. This research has been accomplished within RITA (Research ITalian network on Approximation). All the authors are members of the INdAM Research group GNCS.

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Cavoretto, R., De Rossi, A., Dell’Accio, F. et al. An Efficient Trivariate Algorithm for Tetrahedral Shepard Interpolation. J Sci Comput 82, 57 (2020). https://doi.org/10.1007/s10915-020-01159-3

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  • DOI: https://doi.org/10.1007/s10915-020-01159-3

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