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Hauptverfasser: Bruno, Oscar, Cao, Jinghao
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2504.18699
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author Bruno, Oscar
Cao, Jinghao
author_facet Bruno, Oscar
Cao, Jinghao
contents The rapid and accurate evaluation of convolutions with singular kernels plays crucial roles in a wide range of scientific and engineering applications. Building on the recently introduced Truncated Fourier Filtering method for smooth kernels, this work presents a fast, high-order numerical methodology that extends the approach to singular kernels and non-smooth domains. The method relies on truncated Fourier expansions of a prescribed order for the characteristic function of the integration domain, as well as expansions for the products of characteristic functions and singular functions.
format Preprint
id arxiv_https___arxiv_org_abs_2504_18699
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Fast Singular-Kernel Convolution on General Non-Smooth Domains via Truncated Fourier Filtering
Bruno, Oscar
Cao, Jinghao
Numerical Analysis
The rapid and accurate evaluation of convolutions with singular kernels plays crucial roles in a wide range of scientific and engineering applications. Building on the recently introduced Truncated Fourier Filtering method for smooth kernels, this work presents a fast, high-order numerical methodology that extends the approach to singular kernels and non-smooth domains. The method relies on truncated Fourier expansions of a prescribed order for the characteristic function of the integration domain, as well as expansions for the products of characteristic functions and singular functions.
title Fast Singular-Kernel Convolution on General Non-Smooth Domains via Truncated Fourier Filtering
topic Numerical Analysis
url https://arxiv.org/abs/2504.18699