Northwestern Events Calendar

Nov
27
2017

Yannis Kevrekidis: No Equations, No Variables, No Parameters, No Space and No Time: Data and Machine Learning in Modeling Complex Systems

When: Monday, November 27, 2017
4:00 PM - 5:00 PM CT

Where: Technological Institute, M416, 2145 Sheridan Road, Evanston, IL 60208 map it

Audience: Faculty/Staff - Student - Public - Post Docs/Docs - Graduate Students

Contact: Beth Siculan   (847) 491-3345

Group: McCormick-Colloquia Engineering Sciences and Applied Mathematics

Category: Lectures & Meetings

Description:

Title: No Equations, No Variables, No Parameters, No Space and No Time. Data and Machine Learning in Modeling Complex Systems

Speaker: Yannis Kevrekidis. Bloomberg distinguished professor, Johns Hopkins Professor Emeritus, Princeton

Abstract:

Obtaining predictive dynamical equations from data lies at the heart of science and engineering modeling, and is the linchpin of our technology. In mathematical modeling, one typically progresses from observations of the world (and some  serious thinking!) first to equations for a model, and then to the analysis of the model to make predictions. Good mathematical models give good predictions (and inaccurate ones do not) but the computational tools for analyzing them are the same: algorithms that are typically based on closed form equations.

While the skeleton of the process remains the same, today we witness the development of mathematical techniques that operate directly on observations (data) and appear to circumvent the serious thinking that goes into selecting variables and parameters and deriving accurate 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 building 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. Our 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 traveled by new means.

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