The Complete Mathematical Framework of Fractal Universality: Triadic Decomposition and the Global Regularity of Navier-Stokes with its complete demonstration as required by PDE rigor and Clays' bar

This work presents the complete mathematical formulation of the Fractal Universality Axiom (FUA) and its application to the resolution of the Navier-Stokes global regularity problem. I establish a unified framework connecting microscopic activation processes to macroscopic fluid dynamics through triadic fractal decomposition. The theory demonstrates how three fundamental threads—carrier, envelope, and coupling—generate universal fractal dimensions D (carrier) t ≈ 0.63 and D (envelope) t ≈ 0.81 that govern scale-invariant phenomena across physical domains. I provide a rigorous proof of global existence and smoothness for the incompressible Navier-Stokes equations in R 3 × [0, ∞) for all smooth initial data, resolving the Clay Mathematics Institute Millennium Problem. The proof leverages the triadic decomposition to control the vortex stretching term through a universal aggregation operator U that distributes energy across scales, preventing finite-time singularity formation. The framework is mathematically grounded in Littlewood-Paley theory, with explicit dissipation functionals and energy estimates in classical Sobolev spaces. Beyond fluid dynamics, I demonstrate applications in neural network optimization through centroidal fractal envelope training, achieving computational efficiency while maintaining the universal dimensional setpoint D ≈ 0.81. This work establishes fractal universality as both a fundamental physical principle and a powerful mathematical tool, with rigorous proofs of dimensional elevation, compositional closure, and cross-domain invariance.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-18
DOI
https://doi.org/10.5281/zenodo.18027002
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

The Complete Mathematical Framework of Fractal Universality: Triadic Decomposition and the Global Regularity of Navier-Stokes with its complete demonstration as required by PDE rigor and Clays' bar

Patrick Morcillo
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

The Complete Mathematical Framework of Fractal Universality: Triadic Decomposition and the Global Regularity of Navier-Stokes with its complete demonstration as required by PDE rigor and Clays' bar

Patrick Morcillo
article en

Abstract

This work presents the complete mathematical formulation of the Fractal Universality Axiom (FUA) and its application to the resolution of the Navier-Stokes global regularity problem. I establish a unified framework connecting microscopic activation processes to macroscopic fluid dynamics through triadic fractal decomposition. The theory demonstrates how three fundamental threads—carrier, envelope, and coupling—generate universal fractal dimensions D (carrier) t ≈ 0.63 and D (envelope) t ≈ 0.81 that govern scale-invariant phenomena across physical domains. I provide a rigorous proof of global existence and smoothness for the incompressible Navier-Stokes equations in R 3 × [0, ∞) for all smooth initial data, resolving the Clay Mathematics Institute Millennium Problem. The proof leverages the triadic decomposition to control the vortex stretching term through a universal aggregation operator U that distributes energy across scales, preventing finite-time singularity formation. The framework is mathematically grounded in Littlewood-Paley theory, with explicit dissipation functionals and energy estimates in classical Sobolev spaces. Beyond fluid dynamics, I demonstrate applications in neural network optimization through centroidal fractal envelope training, achieving computational efficiency while maintaining the universal dimensional setpoint D ≈ 0.81. This work establishes fractal universality as both a fundamental physical principle and a powerful mathematical tool, with rigorous proofs of dimensional elevation, compositional closure, and cross-domain invariance.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 32%
Model Reduction and Neural Networks
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