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Spatiotemporal Analysis and Prediction of Laboratory-Generated Turbulence
DescriptionInternal waves below the ocean's surface significantly contribute to heat transfer in the global climate system, and are often studied with laboratory experiments like the Stratified Inclined Duct (SID). These experiments generate 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
moments of an experiment.
The model, a convolutional neural network, is trained on 107 experimental shadowgraph videos and achieves nearly 97% accuracy in classifying turbulence on roughly 7,000 shadowgraph frames. Preliminary work indicates promising results for predictive spatiotemporal modeling, as well as the implementation of the curvelet transform in pre-processing to reduce the model size and improve training times.
moments of an experiment.
The model, a convolutional neural network, is trained on 107 experimental shadowgraph videos and achieves nearly 97% accuracy in classifying turbulence on roughly 7,000 shadowgraph frames. Preliminary work indicates promising results for predictive spatiotemporal modeling, as well as the implementation of the curvelet transform in pre-processing to reduce the model size and improve training times.
Event Type
Workshop
TimeMonday, 13 November 20232:33pm - 2:36pm MST
Location505
State of the Practice
W