Numerical Validation and Phase Transform Algorithm for Prime-Based Synchronization Networks ($N=10^7$)
Abstract
This dataset and software package provide a comprehensive empirical validation of the Prime Phase Transform (PPT) algorithm and the Nedelchev Hypothesis. The project bridges analytical number theory with complex network dynamics, specifically focusing on the synchronization thresholds ($\kappa_c$) of Kuramoto-based oscillators when structured according to Goldbach partitions. Key Contributions: Large-Scale Validation: Numerical verification of the synchronization weight function $\Gamma(N)$ across a range of $10,000,000$ integers. Precision Modeling: Demonstrates a near-perfect correlation ($R^2 = 0.99995$) between prime distribution structures and phase-locking thresholds. Scaling Law: Empirical derivation of the universal scaling constant ($2.539$) and exponent ($0.9327$) for predicting global order in prime-frequency networks. Microscopic Stability: High-resolution analysis of the terminal interval $[9,990,000 - 10,000,000]$, confirming zero local failures in the coupling algorithm. Technical Applications: The provided models and data serve as a computational "Black Box" for engineers working in: Distributed 6G/7G Networks: Precision timing and node synchronization. Neuromorphic Computing: Modeling phase-locking in synthetic neural arrays. Cryptographic Systems: Development of phase-based keys derived from prime density. Files Included: Ultra_Stress_Test_10M.pdf: Full visual and statistical report of the 10-million-point test. Microscopic_Analysis.pdf: Precision data for the final test interval. Validation_Code.py: Source code for reproducing the Kuramoto dynamical simulations. Source Code and Simulations: https://github.com/icobug/prime-synchronization-theorem
Fields of Study & Subjects
Ferroelectric and Negative Capacitance Devices
Nonlinear Dynamics and Pattern Formation
Advanced Memory and Neural Computing
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 4 January 2026
- Document type
- Preprint