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Professur Numerische Mathematik
Professur Numerische Mathematik
Professur Numerische Mathematik 

Mathematical Methods of Uncertainty Quantification (4V, 2Ü) Prof. Ernst, SS 2020

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 ongoing restrictions on teaching as a result of anti-coronavirus measures, this course will take place entirely online at least until May 4, 2020. To participate, please register in the OPAL learning management system following this link.
Wednesday, April 8, 2020 First lecture.

Class Hours

Nummer Name Zeit Raum Details
220000-B60
[Vorlesung]
Montag (Wöchentlich)
11:30-13:00
C46.733
(alt: 2/39/733)
220000-B60A
[Vorlesung]
Donnerstag (Wöchentlich)
11:30-13:00
C46.733
(alt: 2/39/733)
220000-B61
[Übung]
Montag (Wöchentlich)
15:30-17:00
C22.202
(alt: 2/B202)

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.