Synopses & Reviews
Many practical applications require the reconstruction of a multivariate function from discrete, unstructured data. This book gives a self-contained, complete introduction into this subject. It concentrates on truly meshless methods such as radial basis functions, moving least squares, and partitions of unity. The book starts with an overview on typical applications of scattered data approximation, coming from surface reconstruction, fluid-structure interaction, and the numerical solution of partial differential equations. It then leads the reader from basic properties to the current state of research, addressing all important issues, such as existence, uniqueness, approximation properties, numerical stability, and efficient implementation. Each chapter ends with a section giving information on the historical background and hints for further reading. Complete proofs are included, making this perfectly suited for graduate courses on multivariate approximation and it can be used to support courses in computer aided geometric design, and meshless methods for partial differential equations.
Review
"Scattered Data Approximation provides the most complete up-to-date reference on multivariate scattered data approximation from an RBF/mesh-free point of view...I would like to close with a high recommendation of this book. It should be part of anyone's library on modern multivariate approximation techniques."
SIAM Review
Synopsis
A complete self-contained introduction to the theory of scattered data approximation. Written with graduates and researchers in mind, the text brings together much of the necessary background material into a single treatment and provides students with complete proofs to the theory developed within.
Synopsis
A self-contained introduction to the theory of scattered data approximation, suitable for graduates and researchers.
About the Author
Holger Wendland is Associate Professor at the Institute for Numerical and Applied Mathematics at Georg-August-University, Göttingen.
Table of Contents
1. Applications and motivations; 2. Hear spaces and multivariate polynomials; 3. Local polynomial reproduction; 4. Moving least squares; 5. Auxiliary tools from analysis and measure theory; 6. Positive definite functions; 7. Completely monotine functions; 8. Conditionally positive definite functions; 9. Compactly supported functions; 10. Native spaces; 11. Error estimates for radial basis function interpolation; 12. Stability; 13. Optimal recovery; 14. Data structures; 15. Numerical methods; 16. Generalised interpolation; 17. Interpolation on spheres and other manifolds.