Create TECHNICAL_NOTES.md

TECHNICAL_NOTES.md

@@ -0,0 +1,63 @@
+# ⚙️ RFT Adaptive Computing Kernel — Technical Notes (v1.0)
+
+The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** serves as a universal stability and noise-control framework designed for computational environments spanning **CPU, GPU, and TPU architectures**.  
+It measures and adjusts harmonic balance parameters *(QΩ and ζ_sync)* across system workloads to maintain coherence even under fluctuating or high-noise conditions.
+
+---
+
+## 🧠 Core Purpose
+The kernel evaluates and self-adjusts processing stability by simulating perturbations within computational cycles.  
+It applies Rendered Frame Theory’s harmonic laws to map **energy, timing, and coherence** between data operations.
+
+Each run outputs:
+- **QΩ (Harmonic Stability):** Represents amplitude-based consistency across compute cycles.  
+- **ζ_sync (Synchronization Coherence):** Represents phase-alignment and temporal coherence.  
+- **Status:** `nominal`, `perturbed`, or `critical` depending on noise scale and recovery behavior.
+
+---
+
+## ⚡ Operational Domains
+The system supports multiple adaptive profiles:
+| Profile | Description |
+|----------|--------------|
+| **AI / Neural** | Evaluates drift under training noise, backpropagation irregularities, or floating-point jitter. |
+| **SpaceX / Aerospace** | Simulates vibration and latency perturbations found in avionics and telemetry systems. |
+| **Energy / RHES** | Models electrical grid fluctuations and frequency stabilization under dynamic loads. |
+| **Extreme Perturbation** | Pushes systems to their operational noise limits to identify breakdown thresholds. |
+
+---
+
+## 🧮 Internal Algorithmic Overview
+- **Adaptive Baseline:** Maintains a moving equilibrium between QΩ and ζ_sync to resist instability.  
+- **Dynamic Weighting:** Each domain uses a tuned ratio of stability-to-coherence importance.  
+- **Noise Injection:** Synthetic σ values (0.00–0.30) emulate hardware, data, or environmental perturbations.  
+- **Bounded Validation:** All metrics capped at 0.99 to prevent saturation artifacts and false harmonics.  
+- **Soft Learning Memory:** Gradual convergence toward prior equilibrium to simulate recovery intelligence.
+
+---
+
+## 🧰 Usage Notes
+1. Select a **System Profile** and **Noise Distribution** (gauss/uniform).  
+2. Adjust the **Noise Scale (σ)** to simulate conditions.  
+3. Press **Run Simulation**.  
+4. Observe QΩ and ζ_sync behavior — repeated runs with constant σ show adaptive learning trends.
+
+---
+
+## 📊 Interpretation Summary
+| Status | Description | Typical QΩ Range | ζ_sync Range |
+|---------|--------------|------------------|--------------|
+| **Nominal** | Stable harmonic equilibrium | 0.82–0.89 | 0.75–0.88 |
+| **Perturbed** | Transitional adaptive response | 0.79–0.84 | 0.70–0.82 |
+| **Critical** | Instability threshold reached | <0.78 | <0.70 or >0.90 |
+
+---
+
+## 🔐 Legal & Verification
+All metrics are generated within sealed internal algorithms protected under **RFT-IPURL v1.0** (UK/Berne Convention).  
+SHA-512 timestamps are applied to safeguard integrity and originality of simulation logic.
+
+**Author:** Liam Grinstead  
+**Affiliation:** Rendered Frame Theory Systems (RFTSystems)  
+**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)  
+**License:** RFT-IPURL v1.0 — Research validation use only.