Algorithmen für Uncertainty Quantification

What it is about

You will learn methods for quantifying uncertainties in computer-based simulations. In the end you will be able to place central algorithms for Forward Uncertainty Quantification, assess their complexity, and evaluate when which procedures (e.g., Monte Carlo, Quasi-Monte Carlo, stochastic collocation, stochastic Galerkin) are suitable.

What you will be able to do

• Describe fundamental principles and goals of Uncertainty Quantification
• Classify and explain central algorithms for Forward UQ
• Analyze algorithms and assess their complexity
• Explain potential for parallelization and high-performance computing
• Explain how to deal with the Curse of Dimensionality
• List state-of-the-art UQ software and name differences
• Implement simple scenarios with the Python package chaospy

What the module consists of

• VorlesungDelivery of content through lectures and presentations
• Tutorium/ÜbungSolving concrete problems, partly in teamwork, and discussion of examples

Teaching method

• Lectures/Presentationsfor systematic introduction and presentation of the concepts
• Self-study of literatureto engage more deeply with the topics
• Tutorium tasks (individual/team)to ensure practical application and deepening through exercises