Machine Learning in Adaptive FETI-DP – A Comparison of Smart and Random Training Data

Conference paper (2020)

Authors

A. Heinlein University of Cologne, Center for Data and Simulation Science
Axel Klawonn University of Cologne
Martin Lanser University of Cologne
Janine Weber University of Cologne
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Published Date

2020

Language

English

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Abstract

The convergence rate of classical domain decomposition methods for diffusion or elasticity problems usually deteriorates when large coefficient jumps occur along or across the interface between subdomains. In fact, the constant in the classical condition number bounds [11, 12] will depend on the coefficient jump.