Eurika Kaiser
eScience Institute and Mechanical Engineering Department
University of Washington

High-dimensional, nonlinear, multi-scale phenomena, such as turbulence or the spread of infectious diseases, are ubiquitous; however, we still lack a good understanding of these as analytically tractable models remain an exception. The lack of simple equations and unprecedented amount of available high-fidelity data are leading to a paradigm shift in how we interact with complex nonlinear systems. Leading approaches stem from data-driven methods which have the potential to discover new mechanisms, models and control laws and are driven by the tremendous advances in computing power, new sensors and infrastructures, and advanced algorithms in machine learning.

In this talk, I will discuss recent advances in data-driven, equation-free architectures connected to the Perron-Frobenius and Koopman operators leveraging advances in sparsity-promoting techniques and machine learning. Koopman operator theory has emerged as a principled framework to obtain linear embeddings of nonlinear dynamics, enabling the estimation, prediction and control of strongly nonlinear systems using standard linear techniques. In addition, I will discuss work related to statistical modeling in fluids and how to exploit sparsity in dynamical systems for modeling and sensing. The presented work is demonstrated on Hamiltonian systems and different high-dimensional nonlinear systems from fluids.

About the speaker...

Eurika Kaiser received her Diploma degree (M.Sc.) in physical engineering from the Technical University Berlin, Germany, in 2012, and the Ph.D. degree from Universite de Poitiers, France, in 2015. She is currently Moore/Sloan Data Science and Washington Research Foundation Innovation in Data Science Postdoctoral Fellow at the eScience Institute and Mechanical Engineering Department at the University of Washington.