Information Constraint Framework: Computational Falsification of Volumetric Gravity as a Thermodynamic Data-Compression Algorithm

This repository contains the digital artifacts, source code, formal manuscript, and High-Performance Computing (HPC) output data verifying the Information Constraint Framework (ICF) Volumetric Gravity model. Historically, general relativity has modeled gravity as the curvature of a continuous spacetime fabric. By modeling physical space as a discrete, thermodynamically constrained informational matrix, we hypothesize that macroscopic gravity is an algorithmic fail-safe triggered to prevent localized data-buffer overloads. Utilizing High-Performance Computing (HPC) nodes (NVIDIA RTX 4000 Ada, 62 GB RAM, 8 vCPUs), a 1-billion volumetric coordinate manifold was simulated and injected with 10⁷ discrete quantum singularities. The algorithms mathematically demonstrate a proof-of-concept for threshold-triggered volumetric data compression. By applying a Gaussian statistical convolution to discrete volumetric states, the code proves that the universe mathematically defaults to a volumetric statistical smoothing bypass (Rank-6 gravity) upon exceeding local processing thresholds. The supercomputer simulation yielded a verified 98. 9175% reduction in thermodynamic routing strain. Control runs on scaled-down local matrices (1-Million and 8-Million voxels) independently verified >97\% reductions, confirming that the algorithmic bypass is mathematically scale-invariant and increases in efficiency as matrix density scales. Secondary verifications suggest broad implications not only for theoretical physics but for modern computer science, offering a threshold-triggered software blueprint for massive volumetric data compression in hyperscale AI data centers and real-time volumetric rendering architectures. This digital vault includes the primary Jupyter Notebooks (ICFHPCVolumetricFalsification. ipynb), raw HPC Python execution scripts, the. npy exported spatial matrices, terminal execution logs proving the scale-invariance of the thermodynamic reduction, and high-resolution 2D visual extractions of the Rank-6 core slice.