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BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.18.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Penn Engineering Events X-ORIGINAL-URL:https://seasevents.nmsdev7.com X-WR-CALDESC:Events for Penn Engineering Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20180311T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20181104T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20190423T103000 DTEND;TZID=America/New_York:20190423T120000 DTSTAMP:20190305T164831Z CREATED:20190305T164831Z LAST-MODIFIED:20190305T164831Z UID:1433-1556015400-1556020800@seasevents.nmsdev7.com SUMMARY:MEAM Seminar: "Data-driven Discovery of Governing Physical Laws in Engineering\, Physics\, and Biology" DESCRIPTION:A major challenge in the study of dynamical systems is that of model discovery: turning data into models that are not just predictive\, but provide insight into the nature of the underlying dynamical system that generated the data. This problem is made more difficult by the fact that many systems of interest exhibit parametric dependencies and diverse behaviors across multiple time scales. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables\, and (ii) systems for which we have incomplete measurements. For systems with full state measurements\, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data and introduce a sampling method that allows SINDy to scale efficiently to problems with multiple time scales and parametric dependencies. Specifically\, we can discover distinct governing equations at slow and fast scales. For systems with incomplete observations\, we show that the Hankel alternative view of Koopman (HAVOK) method\, based on time-delay embedding coordinates\, can be used to obtain a linear model and Koopman invariant measurement system that nearly perfectly captures the dynamics of nonlinear quasiperiodic systems. We introduce two strategies for using HAVOK on systems with multiple time scales. Together\, our approaches provide a suite of mathematical strategies for reducing the data required to discover and model nonlinear multiscale systems. URL:https://seasevents.nmsdev7.com/event/meam-seminar-data-driven-discovery-of-governing-physical-laws-in-engineering-physics-and-biology/ LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu END:VEVENT END:VCALENDAR