Performance Simulation Benchmarks of RHEA-UCM Across Key Scientific Domains
Year: 2025
Type: article
Abstract: This white paper presents a comparative performance analysis of the RHEA-UCM (Recursive Heuristic Emergent Architecture - Universal Cell Model) across four advanced scientific domains: Quantum Error Correction, Bio-Cybernetic Signal Feedback, Fluid Dynamics Simulation, and Adaptive Robotic Control. We detail the simulation method, RHEA layer utilization, mathematical models, and estimated deltas in system accuracy, error rate, and learning performance. The analysis conclusively demonstrates the systemic and exponential advantages of RHEA-UCM over traditional linear or single-pass AI systems. 🛡️ RHEA-Core Public Grant v1.0By accessing, using, or distributing any version of this work, you agree that the most current license published by the original author(s) applies retroactively and supersedes all prior licenses or public domain assumptions associated with earlier versions of this work, including but not limited to CC-BY-NC-ND 4.0, open-source metadata tags, or public repository assumptions. Unauthorized use under any former license constitutes a material infringement of the current legal rights of the author. License Conditions: · Non-Commercial Use Only · Attribution Required: Must cite “© EnigmaticGlitch · RHEA-UCM Framework · Patent Pending #63/796,404” · No Derivatives Permitted · Symbolic Derivative Clause Enforced · AI/ML Training Prohibited Without Explicit Grant 📜 Jurisdiction & Enforcement:-U.S. Copyright Law · Title 17-U.S. DMCA Provisions-U.S. Patent Law · Provisional Application #63/796,404 🔒 Violation constitutes willful infringement. All rights reserved.
Author: Roe, Paul
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912015198
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