AetherV1.1 Synthetic Airspace Uncertainty Console AetherV1.1 is a UI-first, deterministic system for visualizing synthetic airspace uncertainty. It renders abstract probabilistic density fields and confidence structures in 3D to support human interpretation only. This project is non-operational by design. All telemetry, scenarios, and dynamics are synthetic. There are no live feeds, no control authority, and no actionable outputs.
What this is • A deterministic simulation engine for evolving abstract uncertainty fields • A visual interpretation console (Streamlit + 3D rendering) • A governance-aware metrics layer focused on clarity, uncertainty, and risk • A provenance and audit framework for inspectability and replay • A research prototype for thinking about uncertainty, not acting on it
What this is not • ❌ No targeting or tracking system • ❌ No weapons, guidance, or control logic • ❌ No live data ingestion • ❌ No prediction or forecasting guarantees • ❌ No autonomy or actuation Aether intentionally limits fidelity and realism to avoid false certainty.
Design principles • Determinism first Every run is reproducible via seed and configuration. • Human-in-the-loop by construction The system visualizes uncertainty; it does not resolve it. • Explicit uncertainty Confidence, dispersion, and ambiguity are surfaced rather than hidden. • Governance-aware metrics “Risk” and “clarity” are interpretive signals, not operational truth. • Bounded realism Visuals are expressive but deliberately non-photorealistic.
System architecture Aether is intentionally decomposed into clear layers: core/ Deterministic field evolution and metrics governance/ Event logging, audit chain, safety presets ops/ Snapshot export, replay, reproducibility ui/ Streamlit-based visualization and controls Each layer can be reasoned about independently.
Determinism & reproducibility • All randomness is seeded • Engine advances in discrete ticks • Field evolution is forward-only • Runs can be replayed from: o RNG seed o dispersion / breathing parameters o number of ticks This enables inspection, debugging, and comparative analysis.
Governance & safety posture Aether includes explicit governance features: • Event logging for elevated risk states • Tamper-evident audit chain (SHA-256 hash chaining) • Clear separation between simulation, interpretation, and export • No pathway from visualization to action These features exist to bound misuse, not enable capability escalation.
Visualization approach Aether renders the same synthetic field in multiple ways: • Columnar 3D density field • Volumetric “fog” approximation • 2D cross-sections by altitude • Temporal metric timelines Rendering choices do not introduce new inference. They are alternate lenses on the same underlying state.
Exports The system supports non-operational exports: • JSON run snapshot o Metadata (seed, configuration) o Recent metric history o Event log o Audit chain • CSV metrics o Time-series of clarity, risk, dispersion, etc. Exports are intended for analysis and review, not downstream control.
Running locally pip install -r requirements.txt streamlit run src/aether/app.py Optional: pip install plotly (for volumetric visualization)
Project status • Research / prototype • Non-operational • UI and metrics are illustrative • APIs and structure are stable enough for review This project is shared to demonstrate system design, determinism, and safety-aware thinking, not to propose deployment.
License MIT
Final note Aether is about how uncertainty is represented, not how decisions are made. If a system claims certainty where none exists, it is already unsafe.