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LOCATION:505
DTSTART;TZID=America/Denver:20231113T143300
DTEND;TZID=America/Denver:20231113T143600
UID:submissions.supercomputing.org_SC23_sess442_ws_whpc114@linklings.com
SUMMARY:Spatiotemporal Analysis and Prediction of Laboratory-Generated Tur
 bulence
DESCRIPTION:Jade Buzinski and Jason Yalim (Arizona State University)\n\nIn
 ternal waves below the ocean's surface significantly contribute to heat tr
 ansfer in the global climate system, and are often studied with laboratory
  experiments like the Stratified Inclined Duct (SID). These experiments ge
 nerate large amounts of data, creating expensive storage costs. This work 
 is an effort to reduce the volume of data by developing a machine learning
  model that can accurately classify and predict mixing events in real time
 , enabling researchers to record and save particular \nmoments of an exper
 iment.\n\nThe model, a convolutional neural network, is trained on 107 exp
 erimental shadowgraph videos and achieves nearly 97% accuracy in classifyi
 ng turbulence on roughly 7,000 shadowgraph frames. Preliminary work indica
 tes promising results for predictive spatiotemporal modeling, as well as t
 he implementation of the curvelet transform in pre-processing to reduce th
 e model size and improve training times.\n\nTag: State of the Practice\n\n
 Registration Category: Workshop Reg Pass\n\nSession Chairs: Elsa J. Gonsio
 rowski (Lawrence Livermore National Laboratory (LLNL)) and Mozhgan Kabiri 
 chimeh (NVIDIA Corporation)\n\n
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