Model Order Reduction Methods for linear systems are well studied and many successful methods exist. We will review some and explain more recent advances in Parametric Model Order Reduction. The focus will be on methods where we interpolate certain signicant measures, that are computed for specic values of the parameter by
Radial Basis Function Interpolation. These measures have a disadvantage as they behave like eigenvalues of matrices depending on parameters and we will explain how that can be dealt with in practice. We will furthermore need to introduce a technique to create a medium size model.
The aim of the conference is to bring together researchers in a range of fields within stochastic analysis from all over the world, to survey recent developments, exchange ideas and to foster future collaborations. The main topics include stochastic partial differential equations, measure valued processes, random walks in random media, Dirichlet forms and diffusions on fractals. We will focus on the common theme of developing new foundational methods which will be useful to various areas within stochastic analysis as well as to problems motivated by