SSD - Koumoutsakos Seminar
Prof. Dr. Petros Koumoutsakos - Computing and Data Science Interfaces for Fluid Mechanics
Chair of Computational Sciences, ETH Zürich, Switzerland
We live in exciting times characterized by a unique convergence of Computing and Data Sciences. Novel frameworks fuse data with numerical methods while learning algorithms are deployed on computers with unprecedented capabilities. Can we harness these new capabilities to solve some of the long standing problems in Fluid Mechanics such as turbulence modeling, flow control and energy cascades ? I will discuss our efforts to answer this question, celebrate successes as well as outline failures and open problems. I will demonstrate how Bayesian reasoning can assist model selection in molecular simulations, how long-shirt memory networks (may fail to) predict chaotic systems and how deep reinforcement learning can produce powerful flow control methodologies. I will argue that, while Data and Computing offer wonderful capabilities, it is human thinking that remains the central element in our effort to solve Fluid Mechanics problems.