RolnickLab / constrained-downscaling

A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.

Date Created 2023-01-05 (about a year ago)

Commits 45 (last one about a year ago)

Stargazers 76 (0 this week)

Watchers 2 (0 this week)

Forks 8

License unknown

Ranking

RepositoryStats indexes 598,732 repositories, of these RolnickLab/constrained-downscaling is ranked #355,850 (41st percentile) for total stargazers, and #487,120 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #67,279/120,199.

RolnickLab/constrained-downscaling is also tagged with popular topics, for these it's ranked: super-resolution (#220/324), emulation (#197/273), climate (#69/117)

Other Information

RolnickLab/constrained-downscaling has Github issues enabled, there are 4 open issues and 0 closed issues.

There have been 1 release, the latest one was published on 2023-07-15 (about a year ago) with the name preprint release.

All Topics

climate emulation downscaling super-resolution physics-constrained

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Github watchers over time, collection started in '23

Recent Commit History

45 commits on the default branch (main) since jan '22

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Languages

The only known language in this repository is Python

updated: 2024-12-20 @ 05:29am, id: 585624817 / R_kgDOIufs8Q