Prof Petros Koumoutsakos

On Fluids, Minds and Machines

The study of flow phenomena has been one of the most challenging intellectual pursuits in science and engineering. In the last few decades computing machines have been assisting these pursuits through large-scale simulations, data collection and processing. More recently it is argued that machines that learn may even automate the discovery of solutions for fluid mechanics problems.

I will focus on fish swimming, a hallmark of Lighthill’s research, to argue that a judicious use of machines along with stochastic variations of classical deterministic perspectives is a potent way to address fluid mechanics problems.

Petros Koumoutsakos holds the Chair for Computational Science at ETH Zurich and serves as Fellow of the Collegium Helveticum. He studied Naval Architecture (Diploma-NTU of Athens, M.Eng.-U. of Michigan), Aeronautics and Applied Mathematics (PhD-Caltech). He has conducted post-doctoral studies at the Center for Parallel Computing at Caltech and at the Center for Turbulent Research at Stanford University and NASA Ames. Petros is elected Fellow of the American Society of Mechanical Engineers (ASME), the American Physical Society (APS), the Society of Industrial and Applied Mathematics (SIAM) and the Collegium Helveticum. He has held visiting fellow positions at Caltech, the University of Tokyo, MIT, the Radcliffe Institute of Advanced Study at Harvard University and he is Distinguished Affiliated Professor at TU Munich. He is recipient of the Advanced Investigator Award by the European Research Council and the ACM Gordon Bell prize in Supercomputing. He is elected Foreign Member to the US National Academy of Engineering (NAE). His research interests are on the fundamentals and applications of computing and artificial intelligence to understand, predict and optimize fluid flows in engineering, nanotechnology, and medicine.