Turbulence Control: From Model-Based to Machine Learned

Flow analysis, modeling, and control are at the heart of engineering applications: aeronautics, airborne and ground transport, wind power generation, and industrial processes, to cite a few examples (Brunton and Noack (Appl Mech Rev 67(5):050801:01–48, 2015)). Fluid flows are characterized by high dimensionality, nonlinearity, multiscale, and time delays, which pose challenges to existing theories and methods for analysis, modeling, and control. Methods of big data, machine learning, and artificial intelligence are revolutionizing these fields and are going toward full automation.