deepsphere/deepsphere-cosmo-tf1
A spherical convolutional neural network for cosmology (TFv1).
This project helps cosmologists classify convergence maps, which are representations of matter distribution in the universe. It takes spherical map data as input and outputs a classification indicating which cosmological model best describes the data. Researchers in astrophysics and cosmology who analyze large-scale structure data will find this useful for discriminating between different universe models.
135 stars. No commits in the last 6 months.
Use this if you need to classify spherical data, specifically cosmological convergence maps, into different categories using a neural network.
Not ideal if your data is not spherical, or if you require a simple, out-of-the-box solution without scientific domain knowledge.
Stars
135
Forks
31
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 01, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/deepsphere/deepsphere-cosmo-tf1"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lmcinnes/umap
Uniform Manifold Approximation and Projection
pyRiemann/pyRiemann
Machine learning for multivariate data through the Riemannian geometry of positive definite...
geomstats/geomstats
Computations and statistics on manifolds with geometric structures.
higra/Higra
Hierarchical Graph Analysis
pavlin-policar/openTSNE
Extensible, parallel implementations of t-SNE