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Found 4 papers, 1 papers with code
Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and impact on predictive skill.
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.
