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Reduced Basis Methods - Einzelansicht

Veranstaltungsart Vorlesung Langtext
Veranstaltungsnummer 106363 Kurztext
Semester WiSe 2021/22 SWS 4
Erwartete Teilnehmer/-innen Studienjahr
Max. Teilnehmer/-innen
Credits Belegung Belegpflicht
Sprache englisch
Termine Gruppe: [unbenannt] iCalendar Export für Outlook
  Tag Zeit Rhythmus Dauer Raum Raum-
Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer/-innen
iCalendar Export für Outlook Di. 08:00 bis 10:00 woch bis 25.01.2022  Einsteinstr. 64 - M B 6 (M 6)        
iCalendar Export für Outlook Fr. 08:00 bis 10:00 woch bis 28.01.2022  Einsteinstr. 64 - M B 6 (M 6)        
Gruppe [unbenannt]:

Zugeordnete Person
Zugeordnete Person Zuständigkeit
Rave, Stephan, Dr. verantwort
Abschluss - Studiengang Sem ECTS Bereich Teilgebiet
Master - Mathematik (88 105 13) -
Master - Mathematik (88 105 10) -
Master - Mathematics (88 F23 20) -
Prüfungen / Module
Prüfungsnummer Modul
22001 Vorlesung mit Übungen - Master Mathematik Version 2013
18004 Vorlesung 2 (mit Studienleistung) - Master Mathematik Version 2013
18003 Vorlesung 2 (ohne Studienleistung) - Master Mathematik Version 2013
12001 Lecture - Master Mathematics Version 2020
20003 Lecture 2 - Master Mathematics Version 2020
Zuordnung zu Einrichtungen
Fachbereich 10 Mathematik und Informatik

Many physical, chemical or biological processes can be described with the help of partial differential equations. Since an analytical solution of the equations is rarely possible, numerical discretization methods must be applied in order to be able to analyze and predict the behavior of these processes. However, despite the many technical advances in high-performance computing, for many applications even a numerical solution can only be obtained with considerable effort. It is therefore of great interest to develop model reduction methods which, starting from a given high-dimensional discrete model can provide efficient surrogate models of low dimension that deliver fast and accurate predictions for varying model parameters.

In this lecture we will primarily consider so-called reduced basis methods for parameterized partial differential equations. These methods are based on a (Petrov-)Galerkin projection of the solution onto a suitably chosen low-dimensional subspace of the discrete solution space. The resulting reduced order models can then be studied using well-known techniques from numerics of partial differential equations (Céa lemma, residual-based error estimators, etc.). Using results from approximation theory, appropriate algorithms for constructing the reduced solution space can be devised that guarantee the quality of the resulting reduced order model. In the integrated practical exercises of this course, we will gain hands-on experience with the application of reduced-basis methods to concrete examples using the model-reduction library pyMOR.


The course will start on Tuesday, October 19.


Knowledge of the finite-element method is helpful but not strictly required. For the practical exercises, basic knowledge of the Python programming language is expected.

Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester WiSe 2021/22 , Aktuelles Semester: SoSe 2023