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SUMMARY:Rothschild Lecture: Mathematics for data-driven modeling - The sci
 ence of crystal balls - Yannis Kevrekidis (Princeton University)
DTSTART:20160621T150000Z
DTEND:20160621T160000Z
UID:TALK66535@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:In mathematical modeling one typically progresses from observa
 tions of the world (and some serious thinking!) to equations for a model\,
  and then to the analysis of the model to make predictions. Good&nbsp\;mat
 hematical models give good predictions (and inaccurate ones do not)  > - b
 ut the computational tools for analyzing them are the same: algorithms tha
 t are typically based on closed form equations. While the skeleton of the 
 process remains the same\, today we witness the&nbsp\;development of mathe
 matical techniques that operate directly on observations -data-\, and "cir
 cumvent" the serious thinking that goes into selecting variables and param
 eters and writing equations. The process then may appear to the user a lit
 tle like making predictions by&nbsp\; "looking into a crystal ball". Yet t
 he "serious thinking" is still there and uses the same -and some new- math
 ematics: it goes into&nbsp\;building algorithms that "jump directly" from 
 data to the analysis of the model (which is never available in closed form
 ) so as to make predictions. I will present a couple of efforts that illus
 trate this new path from data to predictions. It really is the same old pa
 th\, but it is&nbsp\;travelled by new means.
LOCATION:Seminar Room 1\, Newton Institute
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