An Adaptive Fast Gauss Transform in Two Dimensions
Journal Article
·
· SIAM Journal on Scientific Computing
- New York Univ. (NYU), New York, NY (United States)
A variety of problems in computational physics and engineering require the convolution of the heat kernel (a Gaussian) with either discrete sources, densities supported on boundaries, or continuous volume distributions. We present a unified fast Gauss transform for this purpose in two dimensions, making use of an adaptive quad-tree discretization on a unit square which is assumed to contain all sources. Our implementation permits either free-space or periodic boundary conditions to be imposed, and is efficient for any choice of variance in the Gaussian.
- Research Organization:
- New York Univ. (NYU), NY (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- FG02-88ER25053
- OSTI ID:
- 1511004
- Journal Information:
- SIAM Journal on Scientific Computing, Vol. 40, Issue 3; ISSN 1064-8275
- Publisher:
- SIAMCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 6 works
Citation information provided by
Web of Science
Web of Science
Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensions
|
journal | October 2018 |
Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensions | preprint | January 2018 |
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