  The design of flow control systems remains a challenge due to the nonlinear
nature of the equations that govern fluid flow. However, recent advances in
computational fluid dynamics (CFD) have enabled the simulation of complex fluid
flows with high accuracy, opening the possibility of using learning-based
approaches to facilitate controller design. We present a method for learning
the forced and unforced dynamics of airflow over a cylinder directly from CFD
data. The proposed approach, grounded in Koopman theory, is shown to produce
stable dynamical models that can predict the time evolution of the cylinder
system over extended time horizons. Finally, by performing model predictive
control with the learned dynamical models, we are able to find a
straightforward, interpretable control law for suppressing vortex shedding in
the wake of the cylinder.
