Mathematical Methods of Uncertainty Quantification (4V, 2Ü) Prof. Ernst, SS 2022
Topics of this Course:
- Mathematical descriptions of uncertainty
- Random differential equations
- Representation of random fields
- Monte Carlo methods
- Stochastic collocation methods
- Bayesian inverse problems
Announcements
| Online Teaching |
Due to COVID restrictions this course will be taught in a hybrid fashion, with
in-person classes and lab sessions which will be streamed via Zoom and available as
recordings online.
Whether participating in-person or online, it is necessary to resister for the course on the OPAL learning management system.
following this link.
|
|---|
| Wednesday, April 6, 2022 |
First lecture. |
|---|
| April 13-14, 2022 |
Both lectures this week will be held only online. |
|---|
Class Hours
Course Materials
Supplementary Literature
UQ, Numerical methods for random differential equations:
- Tim Sullivan: Introduction to Uncertainty Quantification, Springer 2015.
- Gabriel J. Lord, Catherine E. Powell and Tony Shardlow: An Introduction to Computational Stochastic PDEs, Cambridge University Press, 2014.
- Ralph C. Smith: Uncertainty Quantification: Theory, Implementation and Applications, SIAM 2014.
- Dongbin Xiu: Numerical Methods for Stochastic Computations, Princeton University Press 2010.
- Olivier Le Maitre und Omar M. Knio: Spectral Methods for Uncertainty Quantification, Springer 2010.
Background in Statistics and Probability Theory
- David Williams, Weighing the Odds: A Course in Probability and Statistics, Cambridge University Press, 2001.
