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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17443816 |
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Table of Contents:
- <p>Abstract</p> <p>This disclosure presents a novel system and method for the analysis and optimization of large-scale, complex systems. The invention addresses the fundamental limitations of prior art, which are characterized by a reliance on disembodied statistical models (e.g., machine learning) and static representations of physical assets (e.g., conventional digital twins). The disclosed method utilizes a unique, axiomatically-derived 11-dimensional manifold, $M(Y_{11})$, as a universal state-space. This manifold, generated from the principles of Yıldırım's Geometry, possesses an intrinsic "arrow of complexity" that makes it uniquely suited for modeling dynamic systems. The invention models a target system (e.g., a global supply chain, an energy grid) as a "geometrodynamic agent" whose state is represented as a trajectory on the $M(Y_{11})$ manifold. By ingesting multi-modal remote sensing data and projecting it onto this state-space, the system calculates the agent's trajectory and identifies optimal paths of efficiency, known as "geodesics." This geometrodynamic approach provides a first-principles, non-statistical basis for systemic analysis, enabling unprecedented predictive accuracy and optimization capabilities. A cloud-based SaaS platform, LOT-Aegis, is described as the preferred embodiment of the system.</p> <p> </p>