TY - JOUR
T1 - The orthogonal gradients method: A radial basis functions method for solving partial differential equations on arbitrary surfaces
AU - Piret, Cécile
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: The work of this author was supported by a FSR post-doctoral grant from the catholic University of Louvain. Part of the present work was conducted when the author was a Visiting Post-Doctoral Research Assistant at OCCAM (Oxford Centre for Collaborative Applied Mathematics) under support provided by Award No. KUK-C1-013-04 to the University of Oxford, UK, by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2012/5
Y1 - 2012/5
N2 - Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
AB - Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
UR - http://hdl.handle.net/10754/599943
UR - https://linkinghub.elsevier.com/retrieve/pii/S0021999112001477
UR - http://www.scopus.com/inward/record.url?scp=84861231972&partnerID=8YFLogxK
U2 - 10.1016/j.jcp.2012.03.007
DO - 10.1016/j.jcp.2012.03.007
M3 - Article
SN - 0021-9991
VL - 231
SP - 4662
EP - 4675
JO - Journal of Computational Physics
JF - Journal of Computational Physics
IS - 14
ER -