On Natural and Artificial Optimization

For centuries engineers have taken inspiration from nature for design and problem solving. Today’s computing capabilities present the possibility to approach design optimization not from the perspective of imitating existing natural forms, but from the perspective of deploying effective algorithms inspired by natural processes. Petros discussed the development and fusion of learning and evolution algorithms for applications ranging from micro-fluidics to aerodynamics.


Petros Koumoutsakos is Herbert S. Winokur, Jr. Professor of Engineering and Applied Sciences and Area Chair for Applied Mathematics at Harvard’s  John A. Paulson School of Engineering and Applied Sciences (SEAS). He studied Naval Architecture (Diploma-NTU of Athens, M.Eng.-U. of Michigan), Aeronautics and Applied Mathematics (PhD-Caltech) and has served as the Chair of Computational Science at ETH Zurich (1997-2020). 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). He is recipient of the Advanced Investigator Award by the European Research Council and the ACM Gordon Bell prize in Supercomputing. He is elected International 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.