Department of Mechanics: Seminar: Alexander Heinlein 2023: Difference between revisions
(Created page with "=== Domain decomposition methods for highly-heterogeneous problems–Robust coarse spaces and nonlinear preconditioning === ==== [https://searhein.github.io/ Alexander Heinlein] TU Delft, the Netherlands ==== 13 February 2023, 10:00-11:00 CET, Room B-366 @ [https://g.page/stavarnacvut?share Thákurova 7, 166 29 Prague 6] '''Abstract''' Two-level domain decomposition methods, such as two-level Schwarz, finite element tearing and interconnecting - dual primal (FETI-DP...") |
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In this talk, preconditioning techniques for highly-heterogeneous linear and nonlinear problems will be discussed and numerical results for various problems will be reported; the focus is on Schwarz domain decomposition methods, but many approaches can be - with small modifications - applied to other domain decomposition methods as well. | In this talk, preconditioning techniques for highly-heterogeneous linear and nonlinear problems will be discussed and numerical results for various problems will be reported; the focus is on Schwarz domain decomposition methods, but many approaches can be - with small modifications - applied to other domain decomposition methods as well. | ||
[https://youtu.be/DbOPyYk6IHQ Recorded lecture] at YouTube | |||
Latest revision as of 17:44, 15 February 2023
Domain decomposition methods for highly-heterogeneous problems–Robust coarse spaces and nonlinear preconditioning
Alexander Heinlein TU Delft, the Netherlands
13 February 2023, 10:00-11:00 CET, Room B-366 @ Thákurova 7, 166 29 Prague 6
Abstract
Two-level domain decomposition methods, such as two-level Schwarz, finite element tearing and interconnecting - dual primal (FETI-DP), or balancing domain decomposition by constraints (BDDC) methods, are scalable for a large class of homogeneous problems. However, in the presence of highly-heterogeneous coefficients, convergence generally deteriorates. If the coarse space is enhanced by adapted coarse basis functions, robustness can be regained.
Similar observations can be made for highly-heterogeneous nonlinear problems, where both the linear and the nonlinear convergence maybe affected; in this context, a combination of nonlinear domain decomposition methods and robust coarse spaces can recover good linear and nonlinear solver performance.
In this talk, preconditioning techniques for highly-heterogeneous linear and nonlinear problems will be discussed and numerical results for various problems will be reported; the focus is on Schwarz domain decomposition methods, but many approaches can be - with small modifications - applied to other domain decomposition methods as well.
Recorded lecture at YouTube