Explore projects
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Interactive exploration and analysis of large amounts of data from scientific simulations, in-situ visualization and application control are convincing scenarios for explorative sciences. Based on the open source software JupyterLab, a way has been a
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PRACE Workshop - Interactive High-Performance Computing with Jupyter
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CoE-RAISE / FZJ / AI4HPC / AI4HPC
MIT LicenseAI4HPC, part of CoE RAISE, is an open-source library to train AI models with CFD datasets on HPC systems.
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PRACE Workshop - Interactive High-Performance Computing with Jupyter
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esde / machine-learning / AMBS
MIT LicenseAMBS stands for Atmospheric Machine learning Benchmarking System
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Short tutorial about plotting with Pandas in Python
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esde / training / HPSC-Terrsys-2022
Creative Commons Attribution 4.0 InternationalUpdated -
Mapping of ozone data with machine learning methods.
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