Talks

Stein's method is a powerful technique to establish convergence in distribution of a sequence of random variables to a standard Gaussian random variable. After an introduction to this approach, its application to several problems from stochastic geometry is discussed.

This will be an introductory talk on how the fundamental assumptions of quantum mechanics are modeled and how this relies on the spectral theory of unbounded self-adjoint operators on separable Hilbert spaces.

I will introduce myself and present the topic of my master thesis.

In my thesis, I derived a method for model order reduction of parametric linear time-invariant systems. With this method we can compute parametric reduced-order models that are optimal with respect to the $\mathcal{H}_2 \otimes \mathcal{L}_\infty$-error. The method combines interpolatory methods with numerical optimization. We furthermore discuss the computation of the $\mathcal{H}_2 \otimes \mathcal{L}_\infty$-error and have a look at some numerical results.