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Assistant Professor Omer San featured in U.S. Department of Energy article “ASCR Discovery” for his research in machine learning
With problems such as climate and weather modeling, researchers struggle to incorporate the vast range of scales, from globe-circling currents to local eddies. To tackle this problem, Oklahoma State University’s Omer San will apply machine learning to study turbulence in these types of geophysical flows. Researchers must construct a computational grid to run these simulations, but they have to define the scale of the mesh, perhaps 100 kilometers across, to encompass the globe and produce a calculation of manageable size. At that scale, it’s impossible to simulate a range of smaller factors, such as vortices just a few meters wide that can produce important, outsized effects across the whole system because of nonlinear interactions. Machine learning could provide a way to add back in some of these fine details, San says, like software that sharpens a blurry photo.