Why? Physics is rich with beautiful, formal results — but most of them are trapped in PDFs, LaTeX, or lecture notes. That makes it hard to:
- train symbolic/physics-aware ML models,
- build derivation-checking tools,
- or even just teach physics interactively.
THEORIA fills that gap. Each entry includes:
A result name (e.g., Lorentz transformations)
Clean equations (AsciiMath)
Straightforward step-by-step derivation with reasoning
Symbol definitions & assumptions
Programmatic validation using sympy
References, arXiv-style domain tags, and contributor metadata
Everything is in open, self-contained JSON files. No scraping, no PDFs, just clear structured data for physics learners, teachers, and ML devs.
Contributors Wanted: We’re tiny right now and trying to grow. If you’re into physics or symbolic ML:
Add an entry (any result you love)
Review others' derivations
Build tools on top of the dataset
GitHub https://github.com/theoria-dataset/theoria-dataset/
Licensed under CC-BY 4.0, and we welcome educators, students, ML people, or just anyone who thinks physics deserves better data.
somethingsome•11h ago
ManuelSH•8h ago
somethingsome•5h ago
For example, imagine the entry for the standard equation, should all the derivation and symbolic implementation done as a unique entry? It will be difficult to separate it in logical entries that reference each others, and many physical ideas are fundamentally different, leading to divergences.
I have the impression that it should be easier to just parse reference books and format each paragraph/section as an entry, and maybe build a graph. (considering the reference book as authoritative on the subject)