WassimTenachi/PhySO

Health v2 · maintenance only

Adoption not part of the score

1971 stars · 264 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PhySO is a Python library for physical symbolic optimization that uses deep reinforcement learning to discover analytical physical laws from data via symbolic regression. It leverages physical unit constraints and dimensional analysis to reduce the equation search space, and supports fitting a single functional form across multiple datasets.

Use cases

• discover physical equations from experimental data
• perform symbolic regression with unit constraints
• fit one analytical formula across multiple datasets
• recover equations like a damped harmonic oscillator from data points
• run symbolic regression robust to noisy measurements
• benchmark equation discovery on the Feynman lectures dataset

When to choose

• you need interpretable analytical formulas rather than black-box models
• your data has known physical units that can constrain the search
• you want state-of-the-art symbolic regression performance under noise
• you work in Python with PyTorch and want pip/conda installability

When to avoid

• you need general-purpose symbolic regression outside physics or without unit information
• you need a non-Python or GPU-cluster-scale production system
• you want purely numerical curve fitting without symbolic expressions

Facets

library · maturity active

machine-learning deep-learning reinforcement-learning data-science machine-learning deep-learning data-science python cross-platform symbolic-regression equation-discovery dimensional-analysis physics reinforcement-learning pytorch scientific-computing algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="WassimTenachi/PhySO")