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PreprintCC BY 4.0Zenodo

A Rigorous Discrete-Continuum Proof Framework for P̸ = N P via CMNS Infofluid Dynamics, Modulo-9k Sieves, and Dirichlet L-Function Regulators 3D Modulo-9k Discrete Formulation, Potential-Vortical Decomposition, and Neural Network Modeling

  • Herman Jahzeel Martinez Suazo
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Abstract

This is my idea about the P vs NP problem, we have demonstrated that the invariant kinematics written on the Navier-Stokes Equations are valid in any situation where a flow is possible, so the analogy of a "river of data" is valid and applied to computer sciences, the universe is learning and improving, just comparing this data structure as the same behavior found in biological DNA. This analogy will help later how to model NP problems, this is not the solution to the millenium problem, just an analogy used to model neural networks.

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Cite

BibTeXRIS
BibTeX
@misc{suazo2026rigorous,
  title = {{A Rigorous Discrete-Continuum Proof Framework for P̸ = N P via CMNS Infofluid Dynamics, Modulo-9k Sieves, and Dirichlet L-Function Regulators 3D Modulo-9k Discrete Formulation, Potential-Vortical Decomposition, and Neural Network Modeling}},
  author = {Herman Jahzeel Martinez Suazo},
  howpublished = {Zenodo},
  year = {2026},
  month = oct,
  doi = {10.5281/zenodo.23653630},
  url = {https://zenodo.org/records/23653630},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - A Rigorous Discrete-Continuum Proof Framework for P̸ = N P via CMNS Infofluid Dynamics, Modulo-9k Sieves, and Dirichlet L-Function Regulators 3D Modulo-9k Discrete Formulation, Potential-Vortical Decomposition, and Neural Network Modeling
AU  - Herman Jahzeel Martinez Suazo
PY  - 2026
DA  - 2026/10/26
PB  - Zenodo
DO  - 10.5281/zenodo.23653630
UR  - https://zenodo.org/records/23653630
AB  - This is my idea about the P vs NP problem, we have demonstrated that the invariant kinematics written on the Navier-Stokes Equations are valid in any situation where a flow is possible, so the analogy of a "river of data" is valid and applied to computer sciences, the universe is learning and improving, just comparing this data structure as the same behavior found in biological DNA. This analogy will help later how to model NP problems, this is not the solution to the millenium problem, just an analogy used to model neural networks.
ER  -