Numerical Methods for Uncertainty Quantification

What it is about

You learn how to formulate, analyze, and numerically approximate elliptic boundary value problems with random coefficients. This includes modeling and sampling of random fields as well as numerical methods such as Monte Carlo, stochastic collocation, and stochastic Galerkin methods.

What you will be able to do

• Formulation of elliptic boundary value problems with random coefficients
• Analysis of solutions of stochastic partial differential equations
• Approximation and sampling of random functions/fields
• Application of numerical methods for uncertainty quantification (e.g. Monte Carlo, stochastic collocation, stochastic Galerkin)

What the module consists of

• VorlesungConveying concepts, demonstration examples and discussion
• Übungen/Practice sessionsDeepening the content through problem sets and model solutions

Teaching method

• LecturePresentation of content and demonstrative examples
• Exercise classesWorking on problems for deepening and self-assessment
• Assignments for self studyIndependent engagement with topics and literature