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Advanced Topics in PDEs

Description

COURSE OBJECTIVES

Knowledge and understanding of the functional analytic foundation of PDEs with random parameters and discretisation methods for their numerical solution.

 

COURSE 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 course, 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.

 

LEARNING METHODS
Depending on the teaching situation in spring either direct instruction plus exercises (offline) or flipped classroom plus exercises (online).

 

EXAMINATION INFORMATION
Oral exam

 

PREREQUISITES

  • Introduction to Ordinary Differential Equations, Introduction to Partial Differential Equations

People

 

Multerer M.

Course director

Additional information

Semester
Spring
Academic year
2020-2021
ECTS
3
Language
English
Education
Master of Science in Computational Science, Elective course, Lecture, 1st year
Master of Science in Computational Science, Elective course, Lecture, 2nd year