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20232026
most citedMachine learning and domain decomposition methods -- a survey

2 citations · 5 across the 18 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CE2024★ 1 cited

Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations

Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser +2

Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their…

math.NA2024

Nonlinear Two-Level Schwarz Methods: A Parallel Implementation in FROSch

Alexander Heinlein, Kyrill Ho, Axel Klawonn +1

Owing to the ability of nonlinear domain decomposition methods to improve the nonlinear convergence behavior of Newton's method, they have experienced a rise in popularity recently…

cs.CV2024

Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks

Axel Klawonn, Martin Lanser, Janine Weber

In many modern computer application problems, the classification of image data plays an important role. Among many different supervised machine learning models, convolutional neura…

math.NA2024

Nonlinear Monolithic Two-Level Schwarz Methods for the Navier-Stokes Equations

Axel Klawonn, Martin Lanser

Nonlinear domain decomposition methods became popular in recent years since they can improve the nonlinear convergence behavior of Newton's method significantly for many complex pr…

math.NA2024

Adaptive and frugal BDDC coarse spaces for virtual element discretizations of a Stokes problem with heterogeneous viscosity

Tommaso Bevilacqua, Axel Klawonn, Martin Lanser

The virtual element method (VEM) is a family of numerical methods to discretize partial differential equations on general polygonal or polyhedral computational grids. However, the…

cs.CV2024

Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition

Axel Klawonn, Martin Lanser, Janine Weber

Deep convolutional neural networks (CNNs) have been shown to be very successful in a wide range of image processing applications. However, due to their increasing number of model p…

math.NA2024

Monolithic overlapping Schwarz preconditioners for nonlinear finite element simulations of laser beam welding processes

Tommaso Bevilacqua, Axel Klawonn, Martin Lanser +1

Highly resolved finite element simulations of a laser beam welding process are considered. The thermomechanical behavior of this process is modeled with a set of thermoelasticity e…

cs.PF2024★ 1 cited

A Continuous Benchmarking Infrastructure for High-Performance Computing Applications

Christoph Alt, Martin Lanser, Jonas Plewinski +5

For scientific software, especially those used for large-scale simulations, achieving good performance and efficiently using the available hardware resources is essential. It is im…

math.NA2024★ 1 cited

Computational homogenization for aerogel-like polydisperse open-porous materials using neural network--based surrogate models on the microscale

Axel Klawonn, Martin Lanser, Lucas Mager +1

The morphology of nanostructured materials exhibiting a polydisperse porous space, such as aerogels, is very open porous and fine grained. Therefore, a simulation of the deformatio…