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Advanced Topics in PDEs

Description

Partial differential equations are frequently used to model and ultimately simulate physical phenomena. The important aspect with regard to the reliability and relevance of such a simulation is to take into account and to quantify uncertainties arising from unknown parameters and measurement errors. In this class, we will consider the modelling of uncertain parameters in the context of PDEs and introduce state of the art methods for the numerical computation of quantities of interest. In particular, we will address the famous multilevel Monte Carlo method and Bayesian parameter estimation techniques.

 

PREREQUISITES
Introduction to Partial Differential Equations

People

 

Multerer M.

Course director

Additional information

Semester
Spring
Academic year
2019-2020
ECTS
3
Language
English
Education
Master of Science in Computational Science, Elective course, Lecture, 2nd year

Master of Science in Computational Science, Elective course, Lecture, 1st year