CBE Alumni Seminar: Yannis Kevrekidis, Johns Hopkins University, “No Equations, No Variables, No Parameters, No Space and No Time: Data and the Modeling of Complex Systems, Part I"

Content

Yannis Kevrekidis

No Equations, No Variables, No Parameters, No Space and No Time:Data and the Modeling of Complex Systems, Parts  I and II   

Part 1 is on Thursday, 9/24 at 11:30 am
Part 2 is on Friday, 9/25 at 11:30 am in S330-340 Life Science Laboratories with a reception to follow

Yannis Kevrekidis

Bloomberg Distinguished Professor

Applied Mathematics and Statistics, Chemical and Biomolecular Engineering & the Medical School
John Hopkins University

Pomeroy and Betty Perry Smith Professor in Engineering, Emeritus
Professor of Chemical and Biological Engineering, and of Applied and Computational Mathematics Emeritus
Princeton University

Abstract: 

I will give an overview of a research path in data driven modeling of complex systems over the last 35 or so years – from the early days of shallow neural networks and autoencoders for nonlinear dynamical system identification, to the more recent ML-assisted derivation of data driven “emergent” spaces in which to better learn generative PDE laws and accelerate their solution. In all illustrations presented, I will try to point out connections between the “traditional” numerical analysis we know and love, and the more modern data-driven tools and techniques we now have – and some mathematical questions they hopefully make possible for us to answer.

Part I will focus on results from the early 1990s (the last "AI winter") that are  now beeing resurrected and extended. 

Part II will focus more on contributions from the data science side, and on mathematical modeling questions whose study has been enabled precisely because of recent AI software and hardware developments.