Add Pantara v7d — physics law discovery system#211
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Pantara is a matching-pursuit symbolic regression system for physical laws combining a neural oracle with analytical regression. Key properties: - Analytical power-law detection in O(N) via log-log least-squares - 8 function families × 6 transformation spaces - Pre-trained SetEncoder oracle (15k synthetic episodes) - Scikit-learn fit/predict interface; eval_kwargs disables scaling so that positive-domain log-log detection works correctly SRBench results (code frozen, no post-hoc tuning): 132/133 Feynman+Strogatz datasets evaluated R²≥0.90 on 86/132 datasets (65%) R²=1.000 on 37 datasets (pure power laws, <0.2 s each) Mean R²=0.641, median R²=0.970 Source: https://github.com/Yapock22/pantara Install: pip install git+https://github.com/Yapock22/pantara.git Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Pantara is a physics law discovery system that identifies
mathematical structure from raw sensor data using statistical
signature detection, analytical power-law regression (lstsq
in log space), and a specialized trigonometric estimator.
Evaluated on 132 Feynman/Strogatz datasets from SRBench:
Best on multiplicative/power-law equations. Does not cover
Gaussian distributions, Euclidean distances, or differential
equations.
Code: github.com/Yapock22/pantara