The course introduces concepts and methods used in numerical uncertainty quantification. The participants should be able to implement the methods as well as understand the underlying mathematical concepts.
Probability spaces, stochastic processes, random number generation, Gaussian random fields, Monte Carlo related methods, sparse grids and multi-level methods, stochastic PDEs and PDEs with random coefficients, the Bayesian approach to inverse problems.
To receive full credits, participants are expected to take an oral exam (approx. 30 min) covering the topics of the course or to implement a programming exercise with accompanying written documentation.
Not necessary