3 citations · 8 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks
Viktor Grimm, Alexander Heinlein, Axel Klawonn
In recent years, the concept of introducing physics to machine learning has become widely popular. Most physics-inclusive ML-techniques however are still limited to a single geomet…
A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods
Daniel Balzani, Alexander Heinlein, Axel Klawonn +5
A computational framework is presented to numerically simulate the effects of antihypertensive drugs, in particular calcium channel blockers, on the mechanical response of arterial…
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…