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Found 3 papers, 1 papers with code
Data-driven multiscale modeling of subgrid parameterizations in climate models
Subgrid parameterizations, which represent physical processes occurring below the resolution of current climate models, are an important component in producing accurate, long-term predictions for the climate.
An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications.
MotifNet: a motif-based Graph Convolutional Network for directed graphs
Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community.
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