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- [Project] SO-HMNS v1.0.0: Exact Numerical Verification
Hello Physics Forums Community,
I would like to introduce an open-source infrastructure designed for high-precision numerical analysis and verification of infinite-dimensional dynamic systems: SO-HMNS (Sovereign Absolute Invariant Truth Infrastructure).
* Official Repository:
https://github.com/ryujinchoi/so-hmns
####
Academic Positioning
This platform is not an arbitrary automated solver. Instead, it serves as a rigorous numerical analysis and verification framework. It measures whether the Euler-Maclaurin Tail Error of an injectively modeled infinite-dimensional dynamic system honestly diverges at a specific topological critical plane without hardware bit errors.
The core functional-analytic foundation rests on the theorem: "The collapse of compact operator boundedness upon escaping asymmetric domains." This is reproduced at the hardware layer with absolute bit-level fidelity.
####
0.00% Zero-Gap Hardware Sterilization Specification
To eliminate IEEE 754 floating-point errors and virtualized CPU artifacts, the architecture implements an Absolute Closure implemented deeply in Rust:
1. Pure Decimal Mapping: Forces string (str) inputs to entirely bypass standard float register noise.
2. Unbounded Dynamic Precision: Dynamically scales the Decimal context based on a deterministic exponential expansion formula.
3. Thread-Local Context Isolation: Uses localcontext() anchors to block cross-talk during hyper-dense API traffic.
4. Atomic Deep Copy: Prevents memory address reuse corruptions via strict atomic isolation.
5. Register Clearing Guard: Explicitly invokes local_ctx.clear_flags() to physically erase remaining nanosecond bit noise in L1/L2 caches.
####
Deterministic Space Mappings
The engine calculates constants on-the-fly based on the dimension (d), space type, and nonlinearity flags (σ):
* Continuous Manifold (space_type=0): Activates Pure Sobolev Embedding (α = d/2 + 0.5σ) — targeted at Navier-Stokes (3D) and Yang-Mills equations.
* Discrete Graph/Arithmetic (space_type=1): Activates Gauss-Dirichlet Graph Laplacian Spectrum Norm (α = 1/(d+1)) — targeted at mathematical invariants including the Riemann Hypothesis, BSD Conjecture, P vs NP, and Goldbach's Conjecture.
We welcome rigorous code reviews, theoretical discussions, and technical feedback from the community. Feel free to check out our codebase and the latest analytical reports in the repository.
Best regards,
Ryujin Choi
I would like to introduce an open-source infrastructure designed for high-precision numerical analysis and verification of infinite-dimensional dynamic systems: SO-HMNS (Sovereign Absolute Invariant Truth Infrastructure).
* Official Repository:
https://github.com/ryujinchoi/so-hmns
####
Academic PositioningThis platform is not an arbitrary automated solver. Instead, it serves as a rigorous numerical analysis and verification framework. It measures whether the Euler-Maclaurin Tail Error of an injectively modeled infinite-dimensional dynamic system honestly diverges at a specific topological critical plane without hardware bit errors.
The core functional-analytic foundation rests on the theorem: "The collapse of compact operator boundedness upon escaping asymmetric domains." This is reproduced at the hardware layer with absolute bit-level fidelity.
####
0.00% Zero-Gap Hardware Sterilization SpecificationTo eliminate IEEE 754 floating-point errors and virtualized CPU artifacts, the architecture implements an Absolute Closure implemented deeply in Rust:
1. Pure Decimal Mapping: Forces string (str) inputs to entirely bypass standard float register noise.
2. Unbounded Dynamic Precision: Dynamically scales the Decimal context based on a deterministic exponential expansion formula.
3. Thread-Local Context Isolation: Uses localcontext() anchors to block cross-talk during hyper-dense API traffic.
4. Atomic Deep Copy: Prevents memory address reuse corruptions via strict atomic isolation.
5. Register Clearing Guard: Explicitly invokes local_ctx.clear_flags() to physically erase remaining nanosecond bit noise in L1/L2 caches.
####
Deterministic Space MappingsThe engine calculates constants on-the-fly based on the dimension (d), space type, and nonlinearity flags (σ):
* Continuous Manifold (space_type=0): Activates Pure Sobolev Embedding (α = d/2 + 0.5σ) — targeted at Navier-Stokes (3D) and Yang-Mills equations.
* Discrete Graph/Arithmetic (space_type=1): Activates Gauss-Dirichlet Graph Laplacian Spectrum Norm (α = 1/(d+1)) — targeted at mathematical invariants including the Riemann Hypothesis, BSD Conjecture, P vs NP, and Goldbach's Conjecture.
We welcome rigorous code reviews, theoretical discussions, and technical feedback from the community. Feel free to check out our codebase and the latest analytical reports in the repository.
Best regards,
Ryujin Choi