Steven Brunton, Mechanical Engineering
Nathan Kutz, Applied Mathematics
Ph.D., Fluid dynamics, Université de Poitiers, France, 2015
Dipl.-Ing. (M. Sc.), Physical Engineering, Technische Universität Berlin, Germany, 2012
Eurika Kaiser’s research focuses on the development of scalable data-driven algorithms for the construction of low-dimensional, highly predictive models and robust control logic employing machine learning, dimensionality reduction, compressive sensing, data fusion, and uncertainty quantification. Data-driven formation and adaption of models and control laws are particularly important as control generally drives the system away from the behavior where it has been originally characterized.
The modeling and control of multi-scale, high-dimensional, nonlinear dynamical systems, such as turbulent fluid flows, poses a great challenge. While we know the governing equations for some systems, their use in realistic applications is often not feasible. Particularly in turbulence control, we are far away from actually resolving all the scales and eventually exploiting their coupling and interactions for control.
On the other hand, the tremendous advances in recent years in computing power, new sensors and infrastructures allow the collection and real-time access of massive amounts of data. However, the analysis and processing of these huge amounts of data become an increasingly complex task. Moreover, although most of these dynamical systems exhibit low-dimensional patterns and coherent structures which are extremely useful for control, the deluge of data may strain computational resources and conceal the underlying dynamics.
Kaiser believes that leveraging and advancing tools from data science will be transformative for engineering applications, and particularly turbulence control, in the coming years.