
Este preprint introduce VEHICLE 3D con E.I.A.R.(V), un marco gobernado por proyección para estudiar redes relacionales autoestabilizantes. Incluye una formulación matemática, hipótesis de validación H1-H5, una taxonomía operativa de atractores A0-A6 y una comparación computacional reproducible entre regímenes rígido, fluido y gobernado. La versión en español, las fuentes LaTeX y el paquete de reproducibilidad se incluyen como archivos suplementarios.
This preprint introduces VEHICLE 3D with E.I.A.R.(V), a projection-governed framework for studying self-stabilizing relational networks. It includes a mathematical formulation, validation hypotheses H1-H5, an operational attractor taxonomy A0-A6, and a reproducible computational comparison of rigid, fluid, and governed regimes. Spanish version, LaTeX sources, and reproducibility package are included as supplementary files.
graph dynamics, relational dynamical systems, self-stabilizing networks, VEHICLE 3D, VEHICLE Systems Lab, E.I.A.R.(V), AIMTG, projection operator, Lyapunov dissipation, discrete attractors, computational validation
graph dynamics, relational dynamical systems, self-stabilizing networks, VEHICLE 3D, VEHICLE Systems Lab, E.I.A.R.(V), AIMTG, projection operator, Lyapunov dissipation, discrete attractors, computational validation
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