Data-driven discovery and control of dynamical systems

I am a Gordon & Betty Moore Foundation, Alfred P. Sloan Foundation and Washington Research Foundation Innovation in Data Science Postdoctoral Fellow at the eScience Institute and the Department of Mechanical Engineering at the University of Washington, where I work under the supervision of Profs. Steven Brunton (Mechanical Engineering) and Nathan Kutz (Applied Mathematics).

My research is focused on advancing theory and methods for the modeling and control of complex dynamical systems leveraging modern dynamical systems theory, sparsity-promoting techniques and machine learning. The resulting methods and tools address issues in dimensionality reduction, uncertainty quantification, efficient sensing and control with the overarching goal of developing robust and scalable methods for the data-driven control of strongly nonlinear systems.

The modeling and control of multi-scale, high-dimensional, nonlinear dynamical systems, such as turbulence, 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. Data-driven schemes for the discovery and on-line adaption of models and control laws is leading to a paradigm shift in how we interact with complex nonlinear systems with the potential to transform science and engineering applications.