An Adaptive Fast Gauss Transform in Two Dimensions

Wang, Jun; Greengard, Leslie

doi:10.1137/17m1159865

Title: An Adaptive Fast Gauss Transform in Two Dimensions

Journal Article · 03 May 2018 · SIAM Journal on Scientific Computing

DOI: https://doi.org/10.1137/17m1159865 · OSTI ID:1511004

Wang, Jun ^[1]; Greengard, Leslie ^[2]

New York Univ. (NYU), New York, NY (United States); New York University
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.

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Research Organization:: New York Univ. (NYU), New York, NY (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FG02-88ER25053

OSTI ID:: 1511004

Journal Information:: SIAM Journal on Scientific Computing, Journal Name: SIAM Journal on Scientific Computing Journal Issue: 3 Vol. 40; ISSN 1064-8275

Publisher:: SIAMCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (2)

Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensions Wang, Jun; Greengard, Leslie Advances in Computational Mathematics, Vol. 45, Issue 2 https://doi.org/10.1007/s10444-018-9641-5	journal	October 2018
Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensions Wang, Jun; Greengard, Leslie arXiv https://doi.org/10.48550/arxiv.1803.07668	preprint	January 2018

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