nedtaylor/athena
A Fortran-based neural network library for physics-based applications. Alongside standard neural network layer types, it also supports graph-based layers and physics informed neural networks.
This tool helps physicists and materials scientists analyze complex physical systems by building and training neural networks. You provide data like atomic structures or charge densities, and it helps you create models that can understand and predict physical properties or behaviors. It's designed for researchers working with physics-based simulations and data.
Use this if you are a physicist or materials scientist needing to apply neural networks, especially graph-based or physics-informed ones, to your complex data like atomic configurations or charge densities.
Not ideal if you are looking for a general-purpose machine learning tool for domains outside of physics-based applications, or if you don't use Fortran.
Stars
31
Forks
3
Language
Fortran
License
MIT
Category
Last pushed
Dec 10, 2025
Commits (30d)
0
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