scpn-control 0.1.0

SCPN Control — Neuro-symbolic Stochastic Petri Net controller for plasma control

---

scpn-control is a standalone neuro-symbolic control engine that compiles Stochastic Petri Nets into spiking neural network controllers with formal verification guarantees. Extracted from scpn-fusion-core as the minimal 41-file transitive closure of the control pipeline.

Quick Start

pip install -e "."
scpn-control demo --steps 1000
scpn-control benchmark --n-bench 5000

Documentation and Tutorials

• Documentation site: https://anulum.github.io/scpn-control/
• Local docs index: docs/index.md
• Benchmark guide: docs/benchmarks.md
• Notebook tutorials:
  • examples/neuro_symbolic_control_demo.ipynb
  • examples/q10_breakeven_demo.ipynb
  • examples/snn_compiler_walkthrough.ipynb

Build docs locally:

python -m pip install mkdocs
mkdocs serve

Execute all notebooks:

python -m pip install -e ".[viz]" jupyter nbconvert
jupyter nbconvert --to notebook --execute --output-dir artifacts/notebook-exec examples/q10_breakeven_demo.ipynb
jupyter nbconvert --to notebook --execute --output-dir artifacts/notebook-exec examples/snn_compiler_walkthrough.ipynb

Optional notebook (requires sc_neurocore available in environment):

jupyter nbconvert --to notebook --execute --output-dir artifacts/notebook-exec examples/neuro_symbolic_control_demo.ipynb

Features

• Petri Net to SNN compilation -- Translates Stochastic Petri Nets into spiking neural network controllers with LIF neurons and bitstream encoding
• Formal verification -- Contract-based pre/post-condition checking on all control observations and actions
• Sub-millisecond latency -- <1ms control loop with optional Rust-accelerated kernels
• Rust acceleration -- PyO3 bindings for SCPN activation, marking update, Boris integration, SNN pools, and MPC
• Multiple controller types -- PID, MPC, H-infinity, SNN, neuro-cybernetic dual R+Z
• Grad-Shafranov solver -- Free-boundary equilibrium solver with L-mode/H-mode profile support
• Digital twin integration -- Real-time telemetry ingest, closed-loop simulation, and flight simulator
• RMSE validation -- CI-gated regression testing against DIII-D and SPARC experimental reference data
• Disruption prediction -- ML-based predictor with SPI mitigation and halo/RE physics

Architecture

src/scpn_control/
+-- scpn/              # Petri net -> SNN compiler
|   +-- structure.py   #   StochasticPetriNet graph builder
|   +-- compiler.py    #   FusionCompiler -> CompiledNet (LIF + bitstream)
|   +-- contracts.py   #   ControlObservation, ControlAction, ControlTargets
|   +-- controller.py  #   NeuroSymbolicController (main entry point)
+-- core/              # Solver + plant model (clean init, no import bombs)
|   +-- fusion_kernel.py           # Grad-Shafranov equilibrium solver
|   +-- integrated_transport_solver.py  # Multi-species transport
|   +-- scaling_laws.py            # IPB98y2 confinement scaling
|   +-- eqdsk.py                   # GEQDSK/EQDSK file I/O
|   +-- uncertainty.py             # Monte Carlo UQ
+-- control/           # Controllers (optional deps guarded)
|   +-- h_infinity_controller.py   # H-inf robust control
|   +-- fusion_sota_mpc.py         # Model Predictive Control
|   +-- disruption_predictor.py    # ML disruption prediction
|   +-- tokamak_digital_twin.py    # Digital twin
|   +-- tokamak_flight_sim.py      # IsoFlux flight simulator
|   +-- neuro_cybernetic_controller.py  # Dual R+Z SNN
+-- cli.py             # Click CLI

scpn-control-rs/       # Rust workspace (5 crates)
+-- control-types/     # PlasmaState, EquilibriumConfig, ControlAction
+-- control-math/      # LIF neuron, Boris pusher, matrix ops
+-- control-core/      # GS solver, transport, confinement scaling
+-- control-control/   # PID, MPC, H-inf, SNN controller
+-- control-python/    # Slim PyO3 bindings (~474 LOC)

Dependencies

Required	Optional
numpy >= 1.24	matplotlib (pip install -e ".[viz]")
scipy >= 1.10	streamlit (pip install -e ".[dashboard]")
click >= 8.0	torch (pip install -e ".[ml]")
	nengo (pip install -e ".[nengo]")

CLI

scpn-control demo --scenario combined --steps 1000   # Closed-loop control demo
scpn-control benchmark --n-bench 5000                 # PID vs SNN timing benchmark
scpn-control validate                                 # RMSE validation dashboard
scpn-control hil-test --shots-dir ...                 # HIL test campaign

Benchmarks

Python micro-benchmark:

scpn-control benchmark --n-bench 5000 --json-out

Rust Criterion benchmarks:

cd scpn-control-rs
cargo bench --workspace

Benchmark docs: docs/benchmarks.md

Dashboard

pip install -e ".[dashboard]"
streamlit run dashboard/control_dashboard.py

Four tabs: Trajectory Viewer, RMSE Dashboard, Timing Benchmark, Shot Replay.

Rust Acceleration

cd scpn-control-rs
cargo test --workspace

# Build Python bindings
pip install maturin
maturin develop --release

# Verify
python -c "import scpn_control_rs; print('Rust backend active')"

The Rust backend provides PyO3 bindings for:

• PyFusionKernel -- Grad-Shafranov solver
• PySnnPool / PySnnController -- Spiking neural network pools
• PyMpcController -- Model Predictive Control
• PyPlasma2D -- Digital twin
• PyTransportSolver -- Chang-Hinton + Sauter bootstrap
• SCPN kernels -- dense_activations, marking_update, sample_firing

Citation

@software{sotek2026scpncontrol,
  title   = {SCPN Control: Neuro-Symbolic Stochastic Petri Net Controller},
  author  = {Sotek, Miroslav and Reiprich, Michal},
  year    = {2026},
  url     = {https://github.com/anulum/scpn-control},
  license = {AGPL-3.0-or-later}
}

Release and PyPI

Publishing is handled by workflow:

• .github/workflows/publish-pypi.yml

Authors

• Miroslav Sotek — ANULUM CH & LI — ORCID
• Michal Reiprich — ANULUM CH & LI

License

• Concepts: Copyright 1996-2026
• Code: Copyright 2024-2026
• License: GNU AGPL v3

GNU Affero General Public License v3.0 — see LICENSE.

For commercial licensing inquiries, contact: [e-mail]