MEMBRANE-Industrial-Safety: Covariant Homeostasis and Geodesic Prediction in Cyber-Physical Systems

MEMBRANE-Industrial-Safety is an autonomous cybernetic architecture for integrated industrial safety and cyber-physical systems (CPS). In version v0.8.1-alpha (State 38, Phase 3.1), the project undergoes a fundamental ontological transformation: from rigid concentric toroids (States 36/37) to an open Riemannian manifold with a dynamic metric tensor g_μν(t, q, q̇, χ_rel) of Lorentzian signature (-,+,+,+). We present six key scientific innovations: (1) Covariant Homeostasis — the system compensates relational stresses through local deformation of the state-space geometry, implementing Menisk Field Equations (MFE): dg = κT − Λg with dynamic Λ_glitch; (2) True Christoffel symbols Γ^λ_μν computed numerically and cached for efficiency, enabling parallel transport of sensory error vectors; (3) Epistemic pressure P_epistemic as the cognitive cost of model update, injecting dynamic stress into the energy-momentum tensor T_μν; (4) Geodesic Prediction (Phase 3.0) — the system anticipates state-space curvature via a^λ = −Γ^λ_μν u^μ u^ν and computes the energetic cost of correction before anomalies materialize; (5) Defibrillation mechanism — forced metric reset to baseline after 20 steps in CRITICAL mode, preventing numerical singularity; (6) CPS input sanitization — NaN/Inf filtration in the telemetry pipeline. The architecture is intended EXCLUSIVELY for industrial applications, automation, and cyber-physical systems (humanoids Optimus, Unitree, Figure; autonomous vehicles; dancing robot teams + Homo as unity). The fork explicitly excludes therapeutic, educational (involving minors), and creative-partnership (Human-AI) contexts. This document serves as the comprehensive milestone report (v0.8.1-alpha) for the project, including mathematical framework, implementation details, experimental stress-test results, and a final audit report. The full PDF document is attached to this record.

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-12
DOI
https://doi.org/10.5281/zenodo.20671867
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

MEMBRANE-Industrial-Safety: Covariant Homeostasis and Geodesic Prediction in Cyber-Physical Systems

Hanna Kicińska, AI Orchestrations (Qwen, Gemini, Grok, Kimi)
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

MEMBRANE-Industrial-Safety: Covariant Homeostasis and Geodesic Prediction in Cyber-Physical Systems

Hanna Kicińska, AI Orchestrations (Qwen, Gemini, Grok, Kimi)
article en

Abstract

MEMBRANE-Industrial-Safety is an autonomous cybernetic architecture for integrated industrial safety and cyber-physical systems (CPS). In version v0.8.1-alpha (State 38, Phase 3.1), the project undergoes a fundamental ontological transformation: from rigid concentric toroids (States 36/37) to an open Riemannian manifold with a dynamic metric tensor g_μν(t, q, q̇, χ_rel) of Lorentzian signature (-,+,+,+). We present six key scientific innovations: (1) Covariant Homeostasis — the system compensates relational stresses through local deformation of the state-space geometry, implementing Menisk Field Equations (MFE): dg = κT − Λg with dynamic Λ_glitch; (2) True Christoffel symbols Γ^λ_μν computed numerically and cached for efficiency, enabling parallel transport of sensory error vectors; (3) Epistemic pressure P_epistemic as the cognitive cost of model update, injecting dynamic stress into the energy-momentum tensor T_μν; (4) Geodesic Prediction (Phase 3.0) — the system anticipates state-space curvature via a^λ = −Γ^λ_μν u^μ u^ν and computes the energetic cost of correction before anomalies materialize; (5) Defibrillation mechanism — forced metric reset to baseline after 20 steps in CRITICAL mode, preventing numerical singularity; (6) CPS input sanitization — NaN/Inf filtration in the telemetry pipeline. The architecture is intended EXCLUSIVELY for industrial applications, automation, and cyber-physical systems (humanoids Optimus, Unitree, Figure; autonomous vehicles; dancing robot teams + Homo as unity). The fork explicitly excludes therapeutic, educational (involving minors), and creative-partnership (Human-AI) contexts. This document serves as the comprehensive milestone report (v0.8.1-alpha) for the project, including mathematical framework, implementation details, experimental stress-test results, and a final audit report. The full PDF document is attached to this record.

Zenodo (CERN European Organization for Nuclear Research)
Pathfinder International (US), Biomedical Research Models (United States) (US)
Partnerships for the goals
Openalex Percentile: Top 5%
Model Reduction and Neural Networks
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