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Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester WiSe 2021/22 , Aktuelles Semester: SoSe 2023
Reduced Basis Methods    Sprache: englisch    Belegpflicht
Nr.:  106363     Vorlesung     WiSe 2021/22     4 SWS     keine Übernahme    
   Fachbereich: Fachbereich 10 Mathematik und Informatik    
      Master/Mathematik, PO 13 (88105)
  Master/Mathematik, PO 10 (88105)
  Master/Mathematics, PO 20 (88F23)
   Zugeordnete Lehrperson:   Rave verantwort
   Termin: Dienstag   08:00  -  10:00    woch
Ende : 25.01.2022
      Raum :   M B 6 (M 6)   Einsteinstr. 64  
  Freitag   08:00  -  10:00    woch
Ende : 28.01.2022
      Raum :   M B 6 (M 6)   Einsteinstr. 64  

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.