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SUMMARY:Unsupervised classification of experimental shadowgraphs of sheare
 d stratified turbulence - Adrien Lefauve (DAMTP)
DTSTART:20230303T130000Z
DTEND:20230303T140000Z
UID:TALK196396@talks.cam.ac.uk
CONTACT:108503
DESCRIPTION:I will present a method for unsupervised dimensionality reduct
 ion and clustering of a large experimental dataset of over 100 shadowgraph
  movies of shear-driven\, salt-stratified turbulence in the Stratified Inc
 lined Duct (SID) . The method automatically detects edges representative o
 f discrete density interfaces\, extracts a low-dimensional vector of stati
 stics representative of their morphology\, and applies a density-based clu
 stering algorithm. Five clusters are detected and interpreted physically\,
  revealing several classes of turbulence and mixing. These classes of turb
 ulence on instantaneous frames are more quantitative\, objective and subtl
 e than previous human classifications of entire movies into flow regimes (
 wavy\, intermittently turbulent\, fully turbulent). The periods of time sp
 ent in each class vary gradually with the experimental input parameters (t
 he Reynolds number and the tilt of the duct). We explain the potential of 
 this approach to help reveal the state space dynamics of large turbulent d
 atasets.
LOCATION:MR14\,  Centre for Mathematical Sciences\, Wilberforce Road\, Cam
 bridge
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