Add PIR (Physics Intermediate Representation) symbolic regression method

[Qazi-pk]

PIR — Physics Intermediate Representation

Classical symbolic regression engine for automated physical law discovery from data.

Method

• Monomial-basis search with log-linearization gate for power-law detection
• Pairwise structure decomposition, RANSAC, sparse regression
• Iterative residual refinement with Occam complexity penalty
• No neural components — pure classical pipeline

Blind Feynman Tier A results

• 13/44 stable (29.5%) across 5 seeds, v3.4
• Previous baseline: 7/44 (v3.3)

Files

• algorithms/PIR/regressor.py — sklearn-compatible wrapper
• algorithms/PIR/install.sh — installs from public repo (MIT license)
• algorithms/PIR/metadata.yml — method metadata
• algorithms/PIR/requirements.txt — POT (Python Optimal Transport)

Links

• Engine: https://github.com/Qazi-pk/physics-engine (public, MIT)
• Paper: https://doi.org/10.5281/zenodo.19723561

Targeting master per @gAldeia's guidance in #203.

[Qazi-pk]

CI build-and-test for pir fails with ModuleNotFoundError: No module named 'methods.pir' —
the same algorithms/ → methods/ mapping issue that appears to affect other methods as well
(brush, eql, pysr, sklearn also failing). The PIR algorithm files are in algorithms/pir/ per
the submission format. Happy to adjust if a different layout is needed.