[Crib-list] VIRTUAL Seminar... SPEAKER: Kyle Ravi Lennon (MIT)/ CRIBB Seminar @ 12:00 PM - 1:00 PM / Friday, December 2, 2022

[Shirley Entzminger]

    VIRTUAL...

	       COMPUTATIONAL RESEARCH in BOSTON and BEYOND SEMINAR
				  (CRIBB)


   ZOOM meeting info...

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

	Meeting ID: 961 5504 2770

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

DATE:	Friday, December 2, 2022

TIME:	12:00 PM - 1:00 PM



SPEAKER:  Kyle Ravi Lennon  (MIT)


TITLE:	Math, Methods, and Models for Data-Driven Rheology


ABSTRACT:


While data-driven tools and techniques have revolutionized much of the 
scientific and engineering landscape, they have yet to make a 
substantial impact in the field of rheology. Rheological data sets are 
at once too scarce and too diverse to enable traditional machine 
learning approaches — their scarcity a reflection of the time- and 
material-intensive nature of bulk rheometry, and their diversity a 
product of the many rheometric protocols and tools used to characterize 
the mechanical behavior of complex fluids. The success of data-driven 
rheology depends on our ability to simultaneously employ different types 
of experimental data in a unified manner, a notable weakness of many 
common machine learning approaches. In this talk, I will present 
frameworks that bring together rheological data, and demonstrate their 
role in designing data-driven tools for modeling and analyzing complex 
fluids. Among these is a new mathematical construction for asymptotic 
nonlinearities in simple shear flows, called Medium Amplitude Parallel 
Superposition (MAPS) rheology. MAPS reveals both a common embedding for 
many previously disconnected data sets and a new class of data-rich 
experiments. After discussing the applications of this new rheological 
data embedding within machine learning frameworks for model 
identification and material health monitoring, we will develop a new 
data-driven modeling framework for complex fluids in arbitrarily strong 
flows. This scientific machine learning framework combines a universal 
approximator with a frameinvariant viscoelastic constitutive equation, 
allowing rheologists to train admissible models using 
laboratory-accessible data. By construction, this framework is highly 
extensible, and trained models may be deployed scalably in computational 
fluid dynamic workflows, enabling rapid design of engineering systems 
involving complex fluids.

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

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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