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20142024
most citedMG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

3 citations · 3 across the 5 of their papers we have counts for

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5 papers · 1 filter

math.NA2025

Unstructured to structured: geometric multigrid on complex geometries via domain remapping

Nicolas Nytko, Scott MacLachlan, J. David Moulton +3

For domains that are easily represented by structured meshes, robust geometric multigrid solvers can quickly provide the numerical solution to many discretized elliptic PDEs. Howev…

math.NA2025

Automated Runge-Kutta-Nyström time stepping for finite element methods in Irksome

Robert C. Kirby, Scott P. MacLachlan, Pablo D. Brubeck

Irksome is a library based on the Unified Form Language (UFL) that automates the application of Runge-Kutta time-stepping methods for finite element spatial discretizations of part…

math.NA2024

Monolithic Multigrid Preconditioners for High-Order Discretizations of Stokes Equations

Alexey Voronin, Graham Harper, Scott MacLachlan +2

This work introduces and assesses the efficiency of a monolithic MG multigrid framework designed for high-order discretizations of stationary Stokes systems using Taylor-Hood a…

math.NA2024

Exploiting mesh structure to improve multigrid performance for saddle point problems

Lukas Spies, Luke Olson, Scott MacLachlan

In recent years, solvers for finite-element discretizations of linear or linearized saddle-point problems, like the Stokes and Oseen equations, have become well established. There…

math.NA2023

A positivity-preserving unigrid method for elliptic PDEs

Ronald D. Haynes, Scott MacLachlan, Dawei Wang

While constraints arise naturally in many physical models, their treatment in mathematical and numerical models varies widely, depending on the nature of the constraint and the ava…