19 Jupyter notebooks and 31 standalone Python examples. Plus interactive Streamlit studio and WASM browser demo.
| # | Notebook | Topic |
|---|---|---|
| 01 | QueueWaves Retry Storm | Desynchronising cascading retries in distributed systems |
| 02 | Minimal Domain | Simplest possible pipeline: 2 layers, 4 oscillators |
| 03 | Geometry Walk | Random-walk phase coupling on torus geometry |
| 04 | Bio Stub | Multi-scale biological oscillators (cardiac, neural, circadian) |
| 05 | Manufacturing SPC | Statistical process control with phase monitoring |
| 06 | Stuart-Landau Amplitude | Phase + amplitude dynamics, Hopf bifurcation |
| 07 | Policy Petri Net | Declarative policy DSL with formal state machines |
| 08 | Audit Replay | SHA256-chained JSONL audit trail and deterministic replay |
| 09 | Binding Spec | YAML domain binding: topology, oscillators, coupling, drivers |
| 10 | Reporting Adapters | OpenTelemetry, Prometheus, Redis export pipelines |
| 11 | Identity Coherence | SSGF identity model: 6 layers, 30 oscillators |
| 12 | Autotune Pipeline | Automatic frequency ID + coupling estimation from data |
| 13 | SSGF Closure | Self-Stabilising Gauge Field closure loop |
| 14 | Chimera Detection | Coexistent coherent/incoherent cluster identification |
| 15 | Spectral Analysis | Hodge decomposition and Laplacian eigenstructure |
| 16 | Sleep Staging | AASM-compliant sleep classification from R(t) |
| 17 | Power Grid Stability | Inertial Kuramoto on IEEE test networks |
| 18 | Market Regime Detection | Financial synchronisation regimes, crash early warning |
| 19 | Swarmalator Dynamics | Spatial + phase coupling, emergent swarm patterns |
| Example | Description |
|---|---|
| power_grid_stability.py | IEEE 14-bus inertial Kuramoto with swing equation |
| neuroscience_eeg.py | EEG band→phase extraction, seizure detection |
| cardiac_rhythm.py | Gap-junction coupling, arrhythmia detection |
| plasma_control.py | MHD mode coupling, multi-scale control |
| market_regime_detection.py | Financial synchronisation, crash early warning |
| swarmalator_dynamics.py | Spatial + phase coupling, 5 emergent patterns |
| stuart_landau_bifurcation.py | Hopf bifurcation, amplitude death |
| inverse_kuramoto.py | Infer K and ω from observed dynamics |
| hodge_decomposition.py | Split K into gradient/curl/harmonic |
| plasticity_learning.py | Three-factor Hebbian coupling adaptation |
| stochastic_resonance.py | Noise-enhanced coherence, D* auto-tuning |
| supervisor_advantage.py | MPC + regime FSM vs open-loop comparison |
| agent_coordination.py | Multi-agent consensus via phase coupling |
| traffic_flow.py | Signal coordination = phase synchronisation |
| epidemic_sir.py | SIR wave synchronisation |
| scaling_showcase.py | N=4 to N=10,000 performance scaling |
| neurocore_cosimulation.py | SC-NeuroCore ↔ Phase Orchestrator bridge |
| multi_engine_comparison.py | All 12 engines on same problem |
| + 10 more (failure recovery, laser, satellite, audit, gRPC, etc.) | |
streamlit run tools/spo_studio.pyFollow this topic by RSS or confirmed email subscription. Keep a personal reading list.