BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:J. Nathan Kutz (University of Washington) DTSTART:20210916T160000Z DTEND:20210916T170000Z DTSTAMP:20260423T003239Z UID:MPML/53 DESCRIPTION:Title: De ep learning for the discovery of parsimonious physics models\nby J. Na than Kutz (University of Washington) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nA major challenge in the st udy of dynamical systems is that of model discovery: turning data into red uced order models that are not just predictive\, but provide insight into the nature of the underlying dynamical system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear mul tiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the go verning variables\, and (ii) systems for which we have incomplete measurem ents. For systems with full state measurements\, we show that the recent s parse identification of nonlinear dynamical systems (SINDy) method can dis cover governing equations with relatively little data and introduce a samp ling method that allows SINDy to scale efficiently to problems with multip le time scales\, noise and parametric dependencies. For systems with inc omplete observations\, we show that the Hankel alternative view of Koopman (HAVOK) method\, based on time-delay embedding coordinates and the dynami c mode decomposition\, can be used to obtain a linear models and Koopman i nvariant measurement systems that nearly perfectly captures the dynamics o f nonlinear quasiperiodic systems. Neural networks are used in targeted wa ys to aid in the model reduction process. Together\, these approaches prov ide a suite of mathematical strategies for reducing the data required to d iscover and model nonlinear multiscale systems.\n LOCATION:https://researchseminars.org/talk/MPML/53/ END:VEVENT END:VCALENDAR