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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Information-theoretic formulation of causality\, m
odeling and control of turbulence - Adrian Lozano-
Duran (Massachusetts Institute of Technology)
DTSTART;TZID=Europe/London:20220331T113000
DTEND;TZID=Europe/London:20220331T120000
UID:TALK171203AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/171203
DESCRIPTION:The problems of causality\, modeling\, and control
for chaotic\, high-dimensional dynamical systems
are formulated in the language of information theo
ry. The central quantity of interest is the Shanno
n entropy\, which measures the amount of informati
on in the states of the system. Within this framew
ork\, causality in a dynamical system is quantifie
d by the information flux among the variables of i
nterest. Reduced-order modeling is posed as a prob
lem on the conservation of information\, in which
models aim at preserving the maximum amount of rel
evant information from the original system. Simila
rly\, control theory is cast in information-theore
tic terms by envisioning the tandem sensor-actuato
r as a device reducing the unknown information of
the state to be controlled. The new formulation is
applied to address three problems in the causalit
y\, modeling\, and con- trol of turbulence\, which
stands as a primary example of a chaotic\, high d
imensional dynamical system. The applications incl
ude the causality of the energy transfer in the tu
rbulent cascade\, subgrid-scale modeling for large
-eddy simulation\, and flow control for drag reduc
tion in wall-bounded turbulence.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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