首页|Reports Outline Machine Learning Research from National Center for Computational Sciences (Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Ideali zed Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data)
Reports Outline Machine Learning Research from National Center for Computational Sciences (Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Ideali zed Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from Oak Ridge, Tennessee, by NewsRx journalists, research stated, “We introduce a machine learned surrogat e model from high-resolution simulation data to capture the subgrid-scale effect s in dry, stratified atmospheric flows. We use deep neural networks (NNs) to mod el the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation.” Funders for this research include Advanced Scientific Computing Research; Office of Science.
National Center for Computational Scienc esOak RidgeTennesseeUnited StatesNorth and Central AmericaCyborgsEme rging TechnologiesMachine Learning
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