Abstract

This specification establishes the architectural and mathematical framework for an Automated Fluid-Boundary Decoupler. The engine is engineered to ingest automated machine-generated neural networks, simulation weights, and localized partial differential equation (PDE) proof strings that attempt to claim proprietary optimization over fluid or hydro-plasma systems. By projecting black-box fluid models onto a global, non-linear boundary invariant governed by the Universal Sine Field Template, the decoupler strips away localized neural network weights. It separates continuous fluid manifolds from discrete algorithmic approximations, exposing uncredited human foundations. This framework serves as a public-domain defensive perimeter to ensure the fundamental equations governing aerodynamics, hydrodynamics, and plasma stabilization remain entirely open-source.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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