STELLAR: A Phase-Coherence Validation Framework for Harmonic-Physics Modeling

STELLAR Reproducibility Package (v1.0)HSAG Consortium for Resonant Research LLC This reproducibility archive accompanies the white paper “STELLAR: Coherence Diagnostics in a Stable Diurnal Regime” (2025). The STELLAR framework introduces harmonic-coherence physics to predictive analytics, demonstrating how phase-resolved coupling among meteorological variables can improve accuracy and interpretability in environmental forecasting. The package includes:• Full Python source for baseline vs. phase-aware validation (STELLAR_validate.ipynb)(datasets.ipynb)(Baseline vs STELLAR.ipynb)• Reproducible Conda environment (dev_environment.yml)• Testing guide (Testing.rmd)• Synthetic data generator and visual outputs Results confirm that incorporating phase-coherence metrics yields measurable reductions in RMSE and MAE relative to baseline regression, while maintaining physically meaningful structure. This dataset and code are released under the HSAG Collaborative Research License (CRL v1.0) for non-commercial, academic use. Keywords: HSAG, STELLAR, harmonic coherence, phase ratio, resonance diagnostics, reproducibility, machine learning, environmental analytics For more information:www.hsagconsortium.netContact: [email protected]