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This repository contains data, figures, and analysis supporting SymC Power Grid Optimization, which demonstrates predictive grid stability through scale-invariant substrate inheritance. Using synchrophasor data, the work shows early-warning precursors, irreversible degradation, and control protocols grounded in critical-damping physics.

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SymC Infrastructure Stability

This repository contains empirical analysis, figures, and supplementary material examining infrastructure stability through the lens of Symmetrical Convergence (SymC).

The central claim explored here is that large-scale infrastructure systems inherit a critical-damping stability boundary from their underlying physical substrates. Stability, degradation, and failure are analyzed as consequences of alignment or misalignment with this inherited boundary, rather than as isolated control or contingency events.

Electrical power networks serve as the primary empirical testbed due to their continuous instrumentation, physical grounding, and operational scale. The conclusions, however, are not grid-specific.

This repository includes:

Empirical figures derived from real, high-resolution infrastructure data Scale-invariant time-series (“ticker”) plots demonstrating topology-preserving failure dynamics Longitudinal analyses showing irreversible substrate degradation and hysteresis Precursor detection identifying measurable drift well before visible instability Tiered operational doctrines indexed to a single stability coordinate (𝜒) Supplementary derivations and archival material supporting reproducibility

The emphasis is on measurement, structure, and consequence. The analyses and protocols documented here are intended to serve as a stability-layer foundation upon which concrete operational implementations can be built.

Conceptual position within SymC This repository sits at the interface between theory and instrumented reality.

The critical-damping boundary is developed in the foundational SymC work. Substrate inheritance is formalized in Noughts. This repository tests those ideas in systems that are: physically instantiated, spatially distributed, continuously measured, and operationally constrained. Infrastructure systems are treated as composed substrates, not abstractions.

Relationship to other SymC repositories This work is part of the broader SymC research program:

Noughts — Substrate Inheritance Framework https://github.com/SymCUniverse/noughts

SymC — Foundational Boundary Principle https://github.com/SymCUniverse/symc

SymC QFT Extensions https://github.com/SymCUniverse/symc-qft

SymC Oscillator and Field Models https://github.com/SymCUniverse/symc-neutrino

Each repository is self-contained. This one emphasizes empirical validation and operational consequence.

Scope and limits

This repository does not attempt to: provide domain-specific control algorithms, replace detailed system simulation, or model post-collapse nonlinear regimes. It focuses on the pre-collapse stability window, where degradation is detectable, intervention is possible, and system behavior remains interpretable.

Status

Research-grade and archival. Its purpose is to document evidence, methodology, and operational framing clearly enough to support independent evaluation, extension, or critique across infrastructure domains.

Citation If you use or reference this work, please cite the associated paper: APA style

Christensen, N. (2025). SymC power grid optimization: Harnessing scale-invariance and substrate inheritance alignment for predictive infrastructure stability and control. SymC Universe Project. https://doi.org/10.5281/zenodo.XXXXXXXX (Replace XXXXXXXX with the Zenodo DOI once published.)

If you are referencing the repository directly: Christensen, N. (2025). SymC infrastructure stability (Version 1.0) [GitHub repository]. SymC Universe Project. https://github.com/SymCUniverse/infrastructure

BibTeX @software{christensen2025symc_infrastructure, author = {Christensen, Nate}, title = {SymC Infrastructure Stability}, year = {2025}, publisher = {SymC Universe Project}, url = {https://github.com/SymCUniverse/infrastructure} }

@article{christensen2025symc_grid, author = {Christensen, Nate}, title = {SymC Power Grid Optimization: Harnessing Scale-Invariance and Substrate Inheritance Alignment for Predictive Infrastructure Stability and Control}, year = {2025}, journal = {Zenodo}, doi = {10.5281/zenodo.XXXXXXXX} }

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This repository contains data, figures, and analysis supporting SymC Power Grid Optimization, which demonstrates predictive grid stability through scale-invariant substrate inheritance. Using synchrophasor data, the work shows early-warning precursors, irreversible degradation, and control protocols grounded in critical-damping physics.

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