2012
DOI: 10.1016/j.jcp.2011.09.027
|View full text |Cite
|
Sign up to set email alerts
|

Fast directional multilevel summation for oscillatory kernels based on Chebyshev interpolation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

3
98
0

Year Published

2013
2013
2024
2024

Publication Types

Select...
7
1

Relationship

3
5

Authors

Journals

citations
Cited by 60 publications
(101 citation statements)
references
References 18 publications
3
98
0
Order By: Relevance
“…The main conceptual advantage over kernel-dependent decompositions is that the only required knowledge about the kernel is a procedure allowing its evaluation at chosen points. This approach is more recent, and hence less-developed, than the kernel-dependent approach, but oscillatory kernels of type (5) are considered in [30,59], paving the way to application of this "black-box" approach to elastodynamic FMMs.…”
mentioning
confidence: 99%
“…The main conceptual advantage over kernel-dependent decompositions is that the only required knowledge about the kernel is a procedure allowing its evaluation at chosen points. This approach is more recent, and hence less-developed, than the kernel-dependent approach, but oscillatory kernels of type (5) are considered in [30,59], paving the way to application of this "black-box" approach to elastodynamic FMMs.…”
mentioning
confidence: 99%
“…Indeed, as will be shown below, it is generally not a good idea to use the present scheme to represent such kernels in the high-frequency regime. The method can, however, be used in conjunction with the directional FMM scheme in [36], where approximations for kernels of the form g(x)e ık|x| (g smooth) are proposed.…”
Section: Resultsmentioning
confidence: 99%
“…Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php to obtain an approximation with error bounded by ε in the · ∞ norm. Additionally, (26), (27), and (36) can be used to choose the parameters in such a way that the above conditions are satisfied. This can be done by following the steps in Algorithm 1 below.…”
Section: Error Bound For the Full Approximation Equationmentioning
confidence: 99%
“…Examples for Laplace, Gauss, and Stokes kernels and radial basis functions, etc., have been presented. It has also been extended to the oscillatory kernels in [12].…”
mentioning
confidence: 99%
“…The presented FMM formulation has two approximations: (1) the interpolation of the kernel functions which is determined by the interpolation order and (2) the low-rank approximation of the M2L operators which is determined by the target accuracy ε. Studies in [12,11] have shown that the setting ( , ε) = (Acc, 10 −Acc ) results approximately in a relative pointwise L 2 error of ε L2 = 10 −Acc . In the rest of the paper we use this convention to describe the accuracy Acc.…”
mentioning
confidence: 99%