Replaces proprietary symbolic regression tools like Eureqa for scientific data analysis. Uses deep reinforcement learning to recover analytical physical expressions from raw datasets while enforcing dimensional consistency.
Tech Stack
PythonPhysics & Astronomy
Why PhySO?
• Enforces physical unit constraints
• High interpretability of results
• PyTorch-based architecture
Limitations
• High computational cost
• Requires Python proficiency
• Limited to symbolic expressions
3/3/2026
Last Update
263
Forks
12
Issues
MIT
License
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