Vortrag mit dem Titel: Data-driven discovery and control of complex systems
This presentation will describe a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. Next, we will explore the data-driven discovery of intrinsic coordinates via the Koopman operator, in which it is possible to embed nonlinear dynamics in a linear framework, enabling optimal nonlinear control using standard linear techniques. This perspective, combining dynamical systems with machine learning and sparse optimization, is explored with the overarching goal of real-time closed-loop feedback flow control.