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Improving models of ocean turbulence with data-driven methods

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If you have a question about this talk, please contact Christopher Edsall .

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Small-scale turbulent mixing in the ocean is of fundamental importance for the vertical transport of heat, carbon, nutrients and other properties that influence global energy budgets and biological activity. We propose a new probabilistic machine learning method for computing energy dissipation rates that characterise this turbulence from vertical profiles of velocity and density gradients, training and testing our model on numerical simulations of decaying turbulence designed to reproduce conditions found in oceanic flows. Our model outperforms existing theoretical models widely used in oceanographic practice, and additionally offers some insight into the underlying physics.

This talk is part of the RSE Seminars series.

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Metallic Glass MR5, Centre for Mathematical Sciences, Wilberforce Road, Cambridge GN06, William Gates Building. Zoom linkhttps://cam-ac-uk.zoom.us/j/88158575361 Meeting ID: 881 5857 5361 Passcode: 277705

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