The Newsletter 2020-10-06
Invitation Numerical Analysis Seminar February 19th
We are pleased to announce the roster for the upcoming Numerical Analysis Seminars. The seminars will take place in an online setting and you are all cordially invited to attend.
We look forward to seeing you there.
Kind regards on behalf of the organization,
TU Delft | Institute for Computational Sciences and Engineering (DCSE)
Invitation Numerical Analysis Seminar
Our guest is Dr. rer. nat. Alexander Heinlein from University of Stuttgart and will be speaking about:
Fast and Robust Overlapping Schwarz Methods - New Developments and an Efficient Parallel Implementation in Trilinos
Date: February 19th, 2021
Time: 12:30 – 13:30
Register here: an MS Teams invite will be sent one day upfront or can be found in your calendar if you are from the NA section of DIAM.
FROSch (Fast and Robust Overlapping Schwarz) is a framework for parallel Schwarz domain decomposition methods, which is part of the Trilinos package ShyLU. Although being a general framework for the construction and combination of Schwarz operators, FROSch currently focusses on preconditioners that are algebraic in the sense that they can be constructed from a fully assembled, parallel distributed matrix. This is facilitated by the use of extension-based coarse spaces, such as generalized Dryja-Smith-Wildund (GDSW) type coarse spaces.
This talk will cover several Schwarz preconditioning techniques which are currently being developed based on the FROSch package: reduced dimension coarse spaces, multilevel GDSW preconditioners, and monolithic preconditioners for block systems. These approaches are introduced and applied to different problems ranging from a simple Poisson equation to a coupled multiphysics simulations of land ice in Greenland and Antarctica.
Furthermore, a brief overview of related Schwarz preconditioning techniques which are currently being developed but not implemented in FROSch yet will be given. This includes nonlinear two-level Schwarz preconditioning techniques based on Galerkin projections, adaptive coarse spaces for highly heterogeneous problems, as well as novel hybrid preconditioning algorithms combining adaptive coarse spaces and machine learning techniques.attachments: