2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Learning Adaptive Constraints in Nonlinear FETI-DP Methods
Axel Klawonn, Martin Lanser, Janine Weber
While linear FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) is an efficient iterative domain decomposition solver for discretized linear PDEs (partial different…
Machine learning and domain decomposition methods -- a survey
Axel Klawonn, Martin Lanser, Janine Weber
Hybrid algorithms, which combine black-box machine learning methods with experience from traditional numerical methods and domain expertise from diverse application areas, are prog…
A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems
Axel Klawonn, Martin Lanser, Janine Weber
Deep neural networks (DNNs) and, in particular, convolutional neural networks (CNNs) have brought significant advances in a wide range of modern computer application problems. Howe…