Control Meets Learning Seminar

Accurate and efficient reduced-order models are essential to understand, predict, estimate, and control complex, multiscale and nonlinear dynamical systems. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data, complementing the existing theoretical, numerical and experimental efforts. These models should ideally be generalizable, interpretable and based on limited training data. In this talk, I will discuss several modern perspectives on data-driven control of nonlinear systems, including the dynamic mode decomposition (DMD), Koopman operator theory, and the sparse identification of nonlinear dynamics (SINDy) approach. SINDy in particular provides a general framework to discover the governing equations underlying a dynamical system simply from measurement data, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control.