TY - JOUR
T1 - Multilevel interpolation of divergence-free vector fields
AU - Farrell, Patricio
AU - Gillow, Kathryn
AU - Wendland, Holger
N1 - KAUST Repository Item: Exported on 2022-06-03
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: This publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2016/5/2
Y1 - 2016/5/2
N2 - We introduce a multilevel technique for interpolating scattered data of divergence-free vector fields with the help of matrix-valued compactly supported kernels. The support radius at a given level is linked to the mesh norm of the data set at that level. There are at least three advantages of this method: no grid structure is necessary for the implementation, the multilevel approach is computationally cheaper than solving a large one-shot system and the interpolant is guaranteed to be analytically divergence-free. Furthermore, though we will not pursue this here, our multilevel approach is able to represent multiple scales in the data if present. We will prove convergence of the scheme, stability estimates and give a numerical example. For the first time, we will also prove error estimates for derivatives and give approximation orders in terms of the fill distance of the finest data set.
AB - We introduce a multilevel technique for interpolating scattered data of divergence-free vector fields with the help of matrix-valued compactly supported kernels. The support radius at a given level is linked to the mesh norm of the data set at that level. There are at least three advantages of this method: no grid structure is necessary for the implementation, the multilevel approach is computationally cheaper than solving a large one-shot system and the interpolant is guaranteed to be analytically divergence-free. Furthermore, though we will not pursue this here, our multilevel approach is able to represent multiple scales in the data if present. We will prove convergence of the scheme, stability estimates and give a numerical example. For the first time, we will also prove error estimates for derivatives and give approximation orders in terms of the fill distance of the finest data set.
UR - http://hdl.handle.net/10754/678504
UR - https://academic.oup.com/imajna/article-lookup/doi/10.1093/imanum/drw006
UR - http://www.scopus.com/inward/record.url?scp=85020905246&partnerID=8YFLogxK
U2 - 10.1093/imanum/drw006
DO - 10.1093/imanum/drw006
M3 - Article
SN - 1464-3642
VL - 37
SP - 332
EP - 353
JO - IMA Journal of Numerical Analysis
JF - IMA Journal of Numerical Analysis
IS - 1
ER -