17 papers
Mixture of experts surrogate model for the homogenization of open-porous materials
Axel Klawonn, Martin Lanser, Lucas Mager +2
For open-porous materials, incorporating their microstructural properties into mechanical simulations poses a significant challenge for accurately capturing elastic deformation. To…
Learning Adaptive Coarse Spaces Using Transferable Neural Network Models for Linear and Nonlinear Overlapping Domain Decomposition Methods
Axel Klawonn, Martin Lanser, Janine Weber-Hamacher
Domain decomposition methods have been established as efficient and parallel scalable iterative solvers and preconditioners for the solution of large-scale systems arising from the…
A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
Simon Klaes, Axel Klawonn, Natalie Kubicki +4
This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models. An alternating Schwarz domain deco…
Two-level nonlinear Schwarz methods - a parallel implementation with application to nonlinear elasticity and incompressible flow problems
Kyrill Ho, Axel Klawonn, Martin Lanser
Nonlinear Schwarz methods are a type of nonlinear domain decomposition method used as an alternative to Newton's method for solving discretized nonlinear partial differential equat…
Monolithic and Block Overlapping Schwarz Preconditioners for the Incompressible Navier-Stokes Equations
Alexander Heinlein, Axel Klawonn, Jascha Knepper +1
Monolithic preconditioners applied to the linear systems arising during the solution of the discretized incompressible Navier-Stokes equations are typically more robust than precon…
Preconditioning a Fluid--Structure Interaction Problem Using Monolithic and Block Domain Decomposition Methods for the Fluid
Axel Klawonn, Jascha Knepper, Lea SaÃmannshausen
A fluid-structure interaction (FSI) problem is solved via a monolithic coupling of the fluid, structure, and geometry subproblems. The iterative GMRES solver is accelerated with th…