Advanced Topics in PDEs
Knowledge and understanding of the functional analytic foundation of PDEs with random parameters and discretisation methods for their numerical solution.
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.
Depending on the teaching situation in spring either direct instruction plus exercises (offline) or flipped classroom plus exercises (online).
- Introduction to Ordinary Differential Equations, Introduction to Partial Differential Equations
- Master of Science in Computational Science, Elective course, Lecture, 2nd year
- PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (2 ECTS)
- PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (2 ECTS)