Modul
Quantifizierung von Unsicherheiten [M-MATH-104054]
Leistungspunkte
4Turnus
Jedes SommersemesterDauer
1 SemesterSprache
Level
4Version
1Verantwortung
Einrichtung
- KIT-Fakultät für Mathematik
Bestandteil von
Teilleistungen
Identifier | Name | LP |
---|---|---|
T-MATH-108399 | Quantifizierung von Unsicherheiten | 4 |
Erfolgskontrolle(n)
Die Modulprüfung erfolgt in Form einer mündlichen Gesamtprüfung (ca. 30 Minuten).
Qualifikationsziele
After successfully taking part in the module's classes and exams, students have gained knowledge and abilities as described in the "Inhalt" section.
Specifically, students know several parametrization methods for uncertainties. Furthermore, students are able to describe the basics of several solution methods (stochastic collocation, stochastic Galerkin, Monte-Carlo). Students can explain the so-called curse of dimensionality.
Students are able to apply numerical methods to solve engineering problems formulated as algebraic or differential equations with uncertainties. They can name the advantages and disadvantages of each method. Students can judge whether specific methods are applicable to the specific problem and discuss their results with specialists and colleagues. Finally, students are able to implement the above methods in computer codes.
Voraussetzungen
Keine
Inhalt
In this class, we learn to propagate uncertain input parameters through differential equation models, a field called Uncertainty Quantification (UQ). Given uncertain input (parameter values, initial or boundary conditions), how uncertain is the output? The first part of the course ("how to do it") gives an overview on techniques that are used. Among these are:
- Sensitivity analysis
- Monte-Carlo methods
- Spectral expansions
- Stochastic Galerkin method
- Collocation methods, sparse grids
The second part of the course ("why to do it like this") deals with the theoretical foundations of these methods. The so-called "curse of dimensionality" leads us to questions from approximation theory. We look back at the very standard numerical algorithms of interpolation and quadrature, and ask how they perform in many dimensions.
Arbeitsaufwand
Präsenzzeit: 45 Stunden
- Lehrveranstaltung einschließlich studienbegleitender Modulprüfung
Selbststudium: 75 Stunden
- Vertiefung der Studieninhalte durch häusliche Nachbearbeitung des Vorlesungsinhaltes
- Bearbeitung von Übungsaufgaben
- Vertiefung der Studieninhalte anhand geeigneter Literatur und Internetrecherche
- Vorbereitung auf die studienbegleitende Modulprüfung