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SUMMARY:Information-theoretic formulation of causality\, modeling and cont
 rol of turbulence - Adrian Lozano-Duran (Massachusetts Institute of Techno
 logy)
DTSTART:20220331T103000Z
DTEND:20220331T110000Z
UID:TALK171203@talks.cam.ac.uk
DESCRIPTION:The problems of causality\, modeling\, and control for chaotic
 \, high-dimensional dynamical systems are formulated in the language of in
 formation theory. The central quantity of interest is the Shannon entropy\
 , which measures the amount of information in the states of the system. Wi
 thin this framework\, causality in a dynamical system is quantified by the
  information flux among the variables of interest. Reduced-order modeling 
 is posed as a problem on the conservation of information\, in which models
  aim at preserving the maximum amount of relevant information from the ori
 ginal system. Similarly\, control theory is cast in information-theoretic 
 terms by envisioning the tandem sensor-actuator as a device reducing the u
 nknown information of the state to be controlled. The new formulation is a
 pplied to address three problems in the causality\, modeling\, and con- tr
 ol of turbulence\, which stands as a primary example of a chaotic\, high d
 imensional dynamical system. The applications include the causality of the
  energy transfer in the turbulent cascade\, subgrid-scale modeling for lar
 ge-eddy simulation\, and flow control for drag reduction in wall-bounded t
 urbulence.
LOCATION:Seminar Room 1\, Newton Institute
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