[Crib-list] VIRTUAL: CRIBB Seminar: SPEAKER: Cristina Martin-Linares (John Hopkins Univ.) - 12:00 Noon - 1:00 PM -- Friday, Dec. 16, 2022

[Shirley Entzminger]

    VIRTUAL...

        COMPUTATIONAL RESEARCH in BOSTON and BEYOND SEMINAR
			  (CRIBB)


   ZOOM meeting info...

           https://mit.zoom.us/j/96155042770

          ====================================

DATE:	Friday, December 16, 2022

TIME:	12:00 Noon - 1:00 PM


TITLE:	Physics-assisted machine-learning models in fluid mechanics and
agent-based systems




SPEAKER:  Cristina Martin-Linares (The Johns Hopkins University)


ABSTRACT:

The heart of research in engineering entails developing predictive 
dynamical equations from observations. For instance, traditional 
modelling approaches involve developing constitutive equations from 
experiments in material science or developing subgrid scale models for 
coarse-grained equations in fluid dynamics, as these systems require 
detailed simulations that can be computationally expensive. Modern 
data-driven and ML techniques can also be used to discover these 
equations and deduce reduced order models for nominally infinite 
dimensional dynamical systems (PDEs) or stochastic (SDEs), which capture 
the physics well. We can even bypass these equations and instead, 
predict the desired dynamics in a fully data-driven manner.

In this talk, I will focus first on a fluid system with PDE dynamics, 
namely thin films flowing down an inclined plane and exhibiting 
spatio-temporal wave patterns. And second, on SDEs arising in 
agent-based models of a population of interacting agents in a stock 
market, with behaviors similar to disease transmission in the field of 
epidemics. In the first topic, I will show how to develop reduced order 
models and learn PDEs to predict the amplitude of the wave directly from 
data, as well as a new approach to recover the full velocity field from 
partial observations of the amplitude of the wave. For the second 
problem, I will show how we can use these techniques to learn the 
effective Langevin-type SDE that governs the behavior of the 
distribution of agents close to a tipping point where the distributions 
becomes unstable. The developed models are more accurate and have wider 
range of applicability than traditional analytically derived models.

=======================================

For information about the "Computational Research in Boston and Beyond 
Seminar"
(CRIBB), please visit:

	https://math.mit.edu/sites/crib/


=================

Shirley A. Entzminger
Administrative Assistant II
Department of Mathematics
Massachusetts Institute of Technology
77 Massachusetts Avenue
Building 2, Room 350A
Cambridge, MA 02139
PHONE: 	(617) 253-4994
E-mail:	daisymae at math.mit.edu
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