Construction of local reduced spaces for Friedrichs' systems via randomized training

Engwer,Christian; Ohlberger, Mario; Renelt, Lukas

Forschungsartikel in Online-Sammlung (Konferenz) | Peer reviewed

Zusammenfassung

This contribution extends the localized training approach, traditionally employed for multiscale problems and parameterized partial differential equations (PDEs) featuring locally heterogeneous coefficients, to the class of linear, positive symmetric operators, known as Friedrichs' operators. Considering a local subdomain with corresponding oversampling domain we prove the compactness of the transfer operator which maps boundary data to solutions on the interior domain. While a Caccioppoli-inequality quantifying the energy decay to the interior holds true for all Friedrichs' systems, showing a compactness result for the graph-spaces hosting the solution is additionally necessary. We discuss the mixed formulation of a convection-diffusion-reaction problem where the necessary compactness result is obtained by the Picard-Weck-Weber theorem. Our numerical results, focusing on a scenario involving heterogeneous diffusion fields with multiple high-conductivity channels, demonstrate the effectiveness of the proposed method.

Details zur Publikation

Name des RepositoriumsProceedings of the Conference Algoritmy 2024
StatusVeröffentlicht
Veröffentlichungsjahr2024 (21.09.2024)
Sprache, in der die Publikation verfasst istEnglisch
KonferenzCentral-European Conference on Scientific Computing, ALGORITMY, Podbanské, Slowakei
Link zum Volltexthttp://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/2144/1027
Stichwörtermultiscale methods; localized training; Friedrichs’ systems

Autor*innen der Universität Münster

Engwer, Christian
Professur für Anwendungen von partiellen Differentialgleichungen (Prof. Engwer)
Ohlberger, Mario
Professur für Angewandte Mathematik, insbesondere Numerik (Prof. Ohlberger)
Renelt, Lukas
Professur für Angewandte Mathematik, insbesondere Numerik (Prof. Ohlberger)