x66ccff/PSRN

[𝐍𝐚𝐭𝐮𝐫𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐜𝐢𝐞𝐧𝐜𝐞] ⚡️ PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

46
/ 100
Emerging

This project helps scientists, physicists, and researchers discover fundamental physical laws and mathematical expressions hidden within their experimental data. You provide numerical data from your experiments, and it outputs potential symbolic equations that explain the observed relationships. It's designed for anyone in a scientific or engineering field who needs to find underlying mathematical models from large datasets, especially those involving complex physical phenomena.

Available on PyPI.

Use this if you have experimental data and want to automatically uncover the underlying mathematical equations or physical laws that govern the system, rather than trying to guess them manually.

Not ideal if you already know the general form of the equation you are looking for and just need to fit parameters, or if your data is purely qualitative.

physics-discovery scientific-modeling experimental-data-analysis equation-discovery computational-science
Maintenance 10 / 25
Adoption 6 / 25
Maturity 22 / 25
Community 8 / 25

How are scores calculated?

Stars

20

Forks

2

Language

Python

License

MIT

Last pushed

Feb 03, 2026

Commits (30d)

0

Dependencies

12

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