Showing 2 open source projects for "geophysical"

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    Google DeepMind GraphCast and GenCast

    Google DeepMind GraphCast and GenCast

    Global weather forecasting model using graph neural networks and JAX

    ...GraphCast is designed to perform high-resolution atmospheric simulations using the ERA5 dataset from ECMWF, while GenCast extends the approach with diffusion-based ensemble forecasting for probabilistic weather prediction. Both models are built on JAX and integrate advanced neural architectures capable of learning from multi-scale geophysical data represented on icosahedral meshes. The package includes pretrained model weights, normalization statistics, and demonstration notebooks that allow users to replicate and fine-tune weather forecasting experiments in Colab or on Google Cloud TPUs and GPUs.
    Downloads: 8 This Week
    Last Update:
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    NVIDIA Earth2Studio

    NVIDIA Earth2Studio

    Open-source deep-learning framework

    ...It provides a unified API that lets researchers, data scientists, and engineers build complex forecasting and analysis pipelines by combining modular prognostic and diagnostic AI models with a diverse range of real-world data sources such as global forecast systems, reanalysis datasets, and satellite feeds. The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. Users can extend Earth2Studio with optional model packs, advanced data interfaces, statistical operators, and backend integrations that support flexible workflows from simple tests to large-scale operational inference.
    Downloads: 1 This Week
    Last Update:
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