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Autori principali: Kovács, Mihály, Molnár, Gyula, Száraz, Máté András
Natura: Preprint
Pubblicazione: 2026
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Accesso online:https://arxiv.org/abs/2605.02670
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author Kovács, Mihály
Molnár, Gyula
Száraz, Máté András
author_facet Kovács, Mihály
Molnár, Gyula
Száraz, Máté András
contents We consider Gaussian Random Fields on metric graphs defined implicitly as the stationary solution to a fractional SPDE driven by Gaussian white noise. Sampling from the finite element approximation requires the Cholesky factorization of the mass matrix, causing non-linear execution time explosions and massive memory fill-in on large graphs. Hence, we combine Neumann-Neumann graph decomposition with mass matrix lumping and demonstrate empirically, that our approach preserves exact theoretical convergence rates established in [8] while achieving multi-order speedups and massive memory reductions.
format Preprint
id arxiv_https___arxiv_org_abs_2605_02670
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Efficient generation of Gaussian random fields on metric graphs via domain decomposition and mass matrix lumping
Kovács, Mihály
Molnár, Gyula
Száraz, Máté András
Numerical Analysis
We consider Gaussian Random Fields on metric graphs defined implicitly as the stationary solution to a fractional SPDE driven by Gaussian white noise. Sampling from the finite element approximation requires the Cholesky factorization of the mass matrix, causing non-linear execution time explosions and massive memory fill-in on large graphs. Hence, we combine Neumann-Neumann graph decomposition with mass matrix lumping and demonstrate empirically, that our approach preserves exact theoretical convergence rates established in [8] while achieving multi-order speedups and massive memory reductions.
title Efficient generation of Gaussian random fields on metric graphs via domain decomposition and mass matrix lumping
topic Numerical Analysis
url https://arxiv.org/abs/2605.02670