After successful completion of the course, students are able to identify problems with high-dimensional structure. They are equipped to estimate approximation errors as well as the cost of algorithms suitable for those problems.
Neural Networks for PDEs, Monte Carlo related methods, numerics of stochastic PDEs and PDEs with random coefficients,
Blackboard lecture supported by slides.
Vorlesung jeden Donnerstag 14:00-16:00 Uhr (10 Minuten Pause) im Seminarraum 4. Stock grün
Erste Vorlesung am 11.4.2024
Skriptum: https://michaelfeischl.github.io/lecturenotes/highdim.pdf
Oral exam covering the course material
Not necessary
basic course in numerical analysis (knowledge of FEM helps but is not necessary)