An adaptive fast Gauss transform in two dimensions
Numerical Analysis
2017-12-04 v1
Abstract
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.
Cite
@article{arxiv.1712.00380,
title = {An adaptive fast Gauss transform in two dimensions},
author = {Jun Wang and Leslie Greengard},
journal= {arXiv preprint arXiv:1712.00380},
year = {2017}
}
Comments
23 pages, 13 figures