je-santos/ms_net
The MultiScale Network for hierarchical regression (MS-Net) performs 3D regression based on a hierarchical principle: coarse inputs provide broad information about the data, and progressively finer-scale inputs can be used to refine this information.
This tool helps researchers and engineers who work with complex 3D simulations, particularly in fields like porous media flow. It takes large 3D arrays representing material structures or simulation conditions and predicts properties, such as fluid flow or electrical conductivity, efficiently. Users can input data from sources like the Digital Rocks Portal to get refined predictions without excessive computational cost.
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Use this if you need to perform accurate predictions on large, complex 3D simulation data without overwhelming your computational resources.
Not ideal if your data is simple, small, or primarily 1D/2D, as the multi-scale approach's benefits might not outweigh its setup for less complex problems.
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Jupyter Notebook
License
MIT
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Last pushed
Dec 07, 2022
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