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.
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Heinlein, A., Klawonn, A., Lanser, M., Weber, J. (2020). Machine Learning in Adaptive FETI-DP – A Comparison of Smart and Random Training Data. In: , et al. Domain Decomposition Methods in Science and Engineering XXV. DD 2018. Lecture Notes in Computational Science and Engineering, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-030-56750-7_24
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DOI: https://doi.org/10.1007/978-3-030-56750-7_24
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