Geometric Theory of the Universe: Multi-Scale Verification & Universe Engine v3 (Version 11.0)

This release presents Version 11. 0 of the Geometric Theory of the Universe (GTU). It introduces universeₑngineᵥ3. py, a unified, deterministic physics kernel that successfully simulates dynamics across quantum, planetary, and cosmological scales using a single set of geometric principles. Key Innovation: Multi-Scale UnificationUnlike previous iterations, this version demonstrates that the fundamental geometric constants (S=601, 451S = 601, 451S=601, 451 and a=4, 389a = 4, 389a=4, 389) produce valid physical behaviors across 60+ orders of magnitude without parameter fitting: Micro Scale (Quantum): Accurately models electron wavefunction evolution and derives the fine-structure constant (α−1≈137. 036^-1 137. 036α−1≈137. 036) with 0. 000% error relative to CODATA 2022. Macro Scale (Solar System): Simulates stable N-body planetary orbits with correct periods (Earth T=1. 00T=1. 00T=1. 00 yr) and energy conservation. Cosmological Scale (Galactic): Reproduces flat galactic rotation curves and MOND-like acceleration behavior, demonstrating that "dark matter" effects emerge naturally from geometric corrections. Included Files Core Software: universeₑngineᵥ3. py: The main multi-scale physics engine (Micro/Meso/Macro/Cosmic modes). cosmologyₖtm. py: Specialized module for cosmological parameter evolution (ΩmₘΩm, H0H₀H0). verificationₛuite. py: Automated testing framework that validates 10 falsifiable predictions. Documentation: gtuₖtmwhitepaper. pdf: The comprehensive theoretical paper (20 pages) detailing the derivation of constants and the "Game Designer" methodology. README. md: Instructions for installation, usage, and reproducing the simulations. CHANGELOG. md: History of the project's evolution from v9 to v11. Verification Data: verificationᵣesults. json: Machine-readable results of the automated test suite. verificationᵣeport. html: Interactive visual report of the simulation results. MethodologyThis project utilizes an "adversarial AI collaboration" approach. The code and theory were refined through iterative critique and refactoring by multiple AI systems (Claude Sonnet 4. 5, GPT-5. 2, Gemini 3 Pro, DeepAgent) acting as skeptical peer reviewers and engineers, ensuring mathematical rigor and code stability.