GALCIT Colloquium

Many approaches to machine learning have struggled with applications that possess complex process dynamics. I will describe an approach to machine learning of dynamical systems based on Koopman Operator Theory (KOT) that produces generative, predictive, context-aware models amenable to (feedback) control applications. KOT has deep mathematical roots and I will discuss its basic tenets. Its first applications were in fluid mechanics and a number of these will be showcased a number of these, but a number of other examples will be discussed, including use in soft robotics.