Local training and enrichment based on a residual localization strategy
Tim Keil, Mario Ohlberger, Felix Schindler, Julia Schleuß
Research article in edited proceedings (conference) | Peer reviewedAbstract
To efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and local online enrichment with localized error control. For the latter, we adapt the residual localization strategy introduced in [Buhr, Engwer, Ohlberger, Rave, SIAM J. Sci. Comput., 2017] which allows to derive a localized a posteriori error estimator that can be employed to adaptively enrich the reduced solution space locally where needed. Numerical experiments demonstrate the potential of the proposed approach.
Details about the publication
Editors: Frolkovič, P; Mikula, K; Ševčovič, D
Book title: Proceedings of the Conference Algoritmy 2024
Page range: 76-84
Publisher: Jednota slovenských matematikov a fyzikov
Published by: Jednota slovenských matematikov a fyzikov
Place of publication: Bratislava
Title of series: Proceedings of the Conference Algoritmy
Volume of series: 8
Status: Published
Release year: 2024
Conference: Algoritmy 2024, Podbanske, Slovakia
ISBN: 978-80-89829-33-0
Link to the full text: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/2157
Keywords: localized model reduction; randomized training; online enrichment; residual localization
Authors from the University of Münster
| Keil, Tim | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
| Ohlberger, Mario | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) Center for Nonlinear Science Center for Multiscale Theory and Computation (CMTC) |
| Schindler, Felix Tobias | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
| Schleuß, Julia | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
Projects the publication originates from
Duration: 01/01/2019 - 31/12/2025 | 1st Funding period Funded by: DFG - Cluster of Excellence Type of project: Subproject in DFG-joint project hosted at University of Münster |