The modelling of real-world processes from biology, physics or engineering often leads to high-dimensional differential equations with a huge range of spatial and temporal scales. Our research group develops computational methods for the analysis of such multiscale dynamical systems. The main interest lies in the design and the analysis of numerically efficient model reduction and coarse-graining strategies for systems that are externally driven, e.g., by random fluctuations such as white noise or by smooth (feedback) controls.
Projects we are working on include nonequilibrium sampling methods for multiscale diffusions with applications to soft matter, model order reduction for control systems, approximation of non-Markovian dynamics and the stochastic modelling of elastic biomembranes.
For more information please visit our research section.