paulaharder/constrained-downscaling
A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.
This project helps climate scientists and meteorologists generate detailed, high-resolution climate and weather predictions from coarser models. It takes low-resolution climate data (like temperature, precipitation, or water vapor) and outputs statistically upsampled data, ensuring that fundamental physical conservation laws are maintained. This is for researchers and practitioners in climate science who need reliable fine-scale climate information.
No commits in the last 6 months.
Use this if you need to upsample coarse climate or weather model outputs and want to ensure the resulting high-resolution data respects physical conservation laws like mass or energy.
Not ideal if your primary concern is generic image super-resolution without specific physical constraints or if you are not working with climate and weather datasets.
Stars
14
Forks
2
Language
Python
License
—
Category
Last pushed
Jun 08, 2023
Commits (30d)
0
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