BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Yannis Kevrekidis (Johns Hopkins University\, USA) DTSTART:20210615T140000Z DTEND:20210615T150000Z DTSTAMP:20260423T021037Z UID:SNAP/4 DESCRIPTION:Title: No equations\, no variables\, no space\, no time: Data and the modeling of co mplex systems\nby Yannis Kevrekidis (Johns Hopkins University\, USA) a s part of Seminars on Numerics and Applications\n\n\nAbstract\nObtaining p redictive dynamical equations from data lies at the heart of science and e ngineering modeling\, and is the linchpin of our technology. In mathematic al modeling one typically progresses from observations of the world (and s ome serious thinking!) first to equations for a model\, and then to the an alysis of the model to make predictions. Good mathematical models give goo d predictions (and inaccurate ones do not) - but the computational tools f or analyzing them are the same: algorithms that are typically based on clo sed form equations. While the skeleton of the process remains the same\, t oday we witness the development of mathematical techniques that operate di rectly on observations -data-\, and appear to circumvent the serious think ing that goes into selecting variables and parameters and deriving accurat e equations. The process then may appear to the user a little like making predictions by "looking in a crystal ball". Yet the "serious thinking" is still there and uses the same -and some new- mathematics: it goes into bui lding algorithms that jump directly from data to the analysis of the model (which is now not available in closed form) so as to make predictions. Ou r work here presents a couple of efforts that illustrate this "new” path from data to predictions. It really is the same old path\, but it is trav elled by new means.\n LOCATION:https://researchseminars.org/talk/SNAP/4/ END:VEVENT END:VCALENDAR