BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Steve Brunton (University of Washington) DTSTART:20210331T170000Z DTEND:20210331T180000Z DTSTAMP:20260423T003236Z UID:MPML/35 DESCRIPTION:Title: Ma chine learning for Fluid Mechanics\nby Steve Brunton (University of Wa shington) as part of Mathematics\, Physics and Machine Learning (IST\, Lis bon)\n\n\nAbstract\nMany tasks in fluid mechanics\, such as design optimiz ation and control\, are challenging because fluids are nonlinear and exhib it a large range of scales in both space and time. This range of scales ne cessitates exceedingly high-dimensional measurements and computational dis cretization to resolve all relevant features\, resulting in vast data sets and time-intensive computations. Indeed\, fluid dynamics is one of the or iginal big data fields\, and many high-performance computing architectures \, experimental measurement techniques\, and advanced data processing and visualization algorithms were driven by decades of research in fluid mecha nics. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data\, complementing the exis ting theoretical\, numerical\, and experimental efforts in fluid mechanics . In this talk\, we will explore current goals and opportunities for machi ne learning in fluid mechanics\, and we will highlight a number of recent technical advances. Because fluid dynamics is central to transportation\, health\, and defense systems\, we will emphasize the importance of machine learning solutions that are interpretable\, explainable\, generalizable\, and that respect known physics.\n LOCATION:https://researchseminars.org/talk/MPML/35/ END:VEVENT END:VCALENDAR