# LQG Volume Quantization Controller — Research Notes This repository contains prototype code and research artifacts exploring discrete volume eigenvalue management within Loop Quantum Gravity (LQG) frameworks. The material here is exploratory: numerical results and integration outcomes are model outputs that require independent review, reproducibility artifacts, and UQ before being interpreted as engineering-ready capabilities. Where this README previously used production-oriented or absolutist language, sections have been rephrased to emphasize research-stage status, reproduction steps, and limitations. ## Summary — Scope & Purpose - Status: Research prototype (development, validation, and independent review required). - Purpose: Provide code, experiments, and example-run outputs used to investigate polymer corrections, SU(2)-based volume eigenvalues, and cross-repository integration patterns in this workspace. - Not a production system: numerical outputs depend on configuration, random seeds, and solver tolerances. ## What changed (guidance for maintainers) - Replaced production claims with research-stage phrasing. - Added this `Scope, Validation & Limitations` section to clarify how to reproduce and interpret numeric outputs. - Where numeric performance or integration metrics are listed, label them as "example-run" values and supply the scripts and environment used to produce them when publishing results. If you prefer a branch/PR workflow instead of direct updates, I can reapply these edits on a feature branch and open a PR for review. ## Scope, Validation & Limitations Scope - Focuses on exploratory computation of volume eigenvalues V_min = γ l_P³ √(j(j+1)), integration prototypes with other workspace tools, and UQ experiments. Validation & Reproducibility - Repro steps: run the scripts in `examples/` and the UQ analysis under `src/validation/`. - Required artifacts for published claims: the example script, raw outputs (CSV/plots), environment specification (Python/OS), random seeds, and exact commit ids for this repo and any integrated repos. - UQ pointers: `src/validation/` includes starter scripts for Monte Carlo sensitivity and basic uncertainty bounds. Treat reported confidence levels as conditional on the documented configuration. Limitations and Caveats - Numerical results are sensitive to solver tolerances, discretization choices, and parameter initializations; perform sensitivity analyses before generalizing results. - Integration metrics that depend on other repositories require pinning dependent repos to specific commit ids to be reproducible. - Some numeric factors and percentages previously reported are example-run measurements from internal test harnesses. Before citing them externally, include the corresponding raw artifacts and UQ reports. ## Conservative Rewording Examples - "Production-ready" → "Research prototype; further validation required before deployment" - "Complete integration" → "Integration tested in prototype scenarios; reproducibility requires pinned commit ids and environment artifacts" - Specific numeric metrics → "Observed in example-run; see examples/ and src/validation/ for reproduction" ## Quick Repro Steps (example) ```bash # create virtual environment python3 -m venv .venv . .venv/bin/activate pip install -r requirements.txt # run a basic example (labels in output are example-run values) python examples/basic_volume_example.py --seed 42 --out outputs/example_volume.csv # run UQ analysis python src/validation/uq_analysis.py --inputs outputs/example_volume.csv --out outputs/uq_report.json