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Recurrent Neural Network Architecture Search for Geophysical Emulation

Argonne researchers will present a technical paper detailing the development of a scalable neural architecture search to forecast sea surface temperatures using a dataset from the National Oceanic and Atmospheric Administration. Their work to develop surrogate geophysical models has the potential to reduce the large computational cost involved in atmospheric and oceanic modeling.