HYBRID

 Thursday, July 7th 2022

3:45 – 4:45 pm (MT)

Limited Seating available at :T-DO Challenge Conference Room (TA-03-0200-256)

WEBEX 

 Speaker:  Prof. Yannis Kevrekidis

Bloomberg Distinguished Professor 

Chemical and Biomolecular Engineering & Applied Mathematics and Statistics & Medical School, Johns Hopkins

“No equations, no variables, no parameters, no space, no time: Data and the modeling of complex systems”

 

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.

Our work here presents a couple of efforts that illustrate this "new” path from data to predictions. One will be in discovering emergent spaces, in which initially disorganized data can be modeled "easier, better, faster."
The second will be in finding transformations across different models of the same phenomenon - deciding when two systems can be thought of as different observations of the same problem. The third (time permitting) will be in automating the generation of optimal algorithms.