scpn-fusion 1.0.1

SCPN Fusion Core - Tokamak Plasma Physics Simulation and Neuro-Symbolic Control Suite

SCPN Fusion Core

A comprehensive tokamak plasma physics simulation and control suite with neuro-symbolic compilation. SCPN Fusion Core models the full lifecycle of a fusion reactor — from Grad-Shafranov equilibrium and MHD stability through transport, heating, neutronics, and real-time disruption prediction — with optional Rust acceleration via PyO3 and an optional bridge to SC-NeuroCore spiking neural networks.

Architecture

scpn-fusion-core/
├── src/scpn_fusion/           # Python package (46 modules)
│   ├── core/                  # Plasma physics engines
│   │   ├── fusion_kernel.py           Grad-Shafranov + transport solver
│   │   ├── compact_reactor_optimizer  MVR-0.96 compact reactor search
│   │   ├── mhd_sawtooth.py           MHD sawtooth crash simulator
│   │   ├── rf_heating.py             ICRH/ECRH/LHCD heating models
│   │   ├── divertor_thermal_sim.py   Divertor heat-flux solver
│   │   ├── hall_mhd_discovery.py     Hall-MHD two-fluid effects
│   │   ├── sandpile_fusion_reactor   SOC criticality model
│   │   ├── neural_equilibrium.py     Neural-network equilibrium solver
│   │   ├── fno_turbulence_suppressor Fourier Neural Operator turbulence model
│   │   ├── turbulence_oracle.py      ITG/TEM turbulence predictor
│   │   ├── wdm_engine.py             Warm dense matter EOS
│   │   ├── geometry_3d.py            3D flux-surface geometry
│   │   ├── global_design_scanner.py  Multi-objective design space explorer
│   │   └── integrated_transport      Coupled transport solver
│   ├── control/               # Reactor control & AI
│   │   ├── tokamak_flight_sim.py     Real-time flight simulator
│   │   ├── tokamak_digital_twin.py   Digital twin with live telemetry
│   │   ├── fusion_optimal_control    Model-predictive controller
│   │   ├── fusion_sota_mpc.py        State-of-the-art MPC
│   │   ├── disruption_predictor.py   ML disruption early-warning
│   │   ├── spi_mitigation.py         Shattered pellet injection
│   │   ├── fusion_control_room.py    Integrated control room sim
│   │   ├── neuro_cybernetic_controller  SNN-based feedback controller
│   │   └── advanced_soc_fusion_learning Self-organized criticality RL
│   ├── nuclear/               # Nuclear engineering
│   │   ├── blanket_neutronics.py     Tritium breeding ratio solver
│   │   ├── nuclear_wall_interaction  PMI / first-wall damage
│   │   ├── pwi_erosion.py            Plasma-wall erosion model
│   │   └── temhd_peltier.py          Thermoelectric MHD effects
│   ├── diagnostics/           # Synthetic diagnostics
│   │   ├── synthetic_sensors.py      Virtual instrument suite
│   │   └── tomography.py             Soft X-ray tomographic inversion
│   ├── engineering/           # Balance of plant
│   │   └── balance_of_plant.py       Thermal cycle, turbine, cryo
│   ├── scpn/                  # Neuro-symbolic compiler
│   │   ├── compiler.py               Petri nets → stochastic neurons
│   │   ├── controller.py             SNN-driven plasma control
│   │   ├── structure.py              Petri net data structures
│   │   ├── contracts.py              Formal verification contracts
│   │   └── artifact.py               Compilation artifact storage
│   ├── hpc/                   # High-performance computing
│   │   └── hpc_bridge.py             C++/Rust FFI bridge
│   └── ui/                    # Dashboard
│       └── app.py                    Streamlit real-time dashboard
├── scpn-fusion-rs/            # Rust workspace (10 crates)
│   ├── crates/
│   │   ├── fusion-types/      # Shared data types
│   │   ├── fusion-math/       # Linear algebra, FFT, interpolation
│   │   ├── fusion-core/       # Grad-Shafranov, transport in Rust
│   │   ├── fusion-physics/    # MHD, heating, turbulence
│   │   ├── fusion-nuclear/    # Neutronics, wall erosion
│   │   ├── fusion-engineering/ # Balance of plant
│   │   ├── fusion-control/    # PID, MPC, disruption predictor
│   │   ├── fusion-diagnostics/ # Sensor models
│   │   ├── fusion-ml/         # Inference engine
│   │   └── fusion-python/     # PyO3 bindings → scpn_fusion_rs.pyd
│   └── Cargo.toml             # Workspace manifest
├── tests/                     # Python test suite
├── docs/                      # Technical documentation
├── validation/                # ITER validation configurations
├── calibration/               # Optimization tools
└── schemas/                   # JSON schemas

Quick Start

# Clone
git clone https://github.com/anulum/scpn-fusion-core.git
cd scpn-fusion-core

# Install (Python)
pip install -e .

# Run a simulation
python run_fusion_suite.py kernel       # Grad-Shafranov equilibrium
python run_fusion_suite.py optimizer    # Compact reactor search (MVR-0.96)
python run_fusion_suite.py flight       # Tokamak flight simulator
python run_fusion_suite.py neural       # Neural equilibrium solver

# Run tests
pytest tests/ -v

Rust Acceleration (Optional)

cd scpn-fusion-rs
cargo build --release
cargo test

# Build Python bindings (requires maturin)
pip install maturin
cd crates/fusion-python
maturin develop --release

The Python package auto-detects the Rust extension and falls back to NumPy if unavailable.

Testing

# Python unit + property-based tests
pip install -e ".[dev]"
pytest tests/ -v

# Rust unit + property-based tests
cd scpn-fusion-rs
cargo test --all-features

# Rust benchmarks
cargo bench

The test suites include property-based tests powered by Hypothesis (Python) and proptest (Rust), covering numerical invariants, topology preservation, and solver convergence properties.

Tutorial Notebooks

Notebook	Description
01_compact_reactor_search	MVR-0.96 compact reactor optimizer walkthrough
02_neuro_symbolic_compiler	Petri net → stochastic neuron compilation pipeline
03_grad_shafranov_equilibrium	Free-boundary equilibrium solver tutorial
04_divertor_and_neutronics	Divertor heat flux & tritium breeding ratio

26 Simulation Modes

Mode	Description
kernel	Grad-Shafranov equilibrium + coupled transport
flight	Real-time tokamak flight simulator
optimal	Model-predictive optimal control
learning	Self-organized criticality reinforcement learning
digital-twin	Live digital twin with telemetry
control-room	Integrated control room simulation
sandpile	SOC sandpile criticality model
nuclear	Plasma-wall interaction & first-wall damage
breeding	Tritium breeding blanket neutronics
safety	ML disruption predictor + early warning
optimizer	Compact reactor design search (MVR-0.96)
divertor	Divertor thermal load simulation
diagnostics	Synthetic diagnostic instrument suite
sawtooth	MHD sawtooth crash dynamics
neural	Neural-network equilibrium solver
geometry	3D flux-surface geometry
spi	Shattered pellet injection mitigation
scanner	Multi-objective global design scanner
heating	RF heating (ICRH / ECRH / LHCD)
wdm	Warm dense matter equation of state
quantum	Quantum computing bridge
q-control	Quantum control agent
neuro-control	SNN-based cybernetic controller
neuro-quantum	Neuro-quantum hybrid controller
lazarus	Lazarus protocol bridge
director	Director AI interface
vibrana	Vibrana resonance bridge

Minimum Viable Reactor (MVR-0.96)

The compact reactor optimizer (python run_fusion_suite.py optimizer) performs multi-objective design-space exploration to find the smallest tokamak configuration that achieves Q >= 10 ignition. The "0.96" refers to the normalized minor radius target. Key parameters explored:

• Major/minor radius, elongation, triangularity
• Magnetic field strength, plasma current
• Heating power allocation (NBI, ICRH, ECRH)
• Tritium breeding ratio constraints
• Divertor heat-flux limits

Neuro-Symbolic Compiler

The scpn/ subpackage implements a Petri net → stochastic neuron compiler:

1. Petri Net Definition — plasma control logic expressed as place/transition nets with formal contracts
2. Compilation — Petri net transitions mapped to stochastic LIF neurons (using SC-NeuroCore when available, NumPy fallback otherwise)
3. Execution — SNN-driven real-time plasma control with sub-millisecond latency
4. Verification — formal contract checking on compiled artifacts

SC-NeuroCore Integration

SCPN Fusion Core has an optional dependency on sc-neurocore. When installed, the neuro-symbolic compiler uses hardware-accurate stochastic LIF neurons and Bernoulli bitstream encoding. Without it, all paths fall back to NumPy float computation:

try:
    from sc_neurocore import StochasticLIFNeuron, generate_bernoulli_bitstream
    _HAS_SC_NEUROCORE = True
except ImportError:
    _HAS_SC_NEUROCORE = False  # NumPy float-path fallback

Rust Workspace

The scpn-fusion-rs/ directory contains a 10-crate Rust workspace that mirrors the Python package structure. Key features:

• Performance: opt-level = 3, fat LTO, single codegen unit for maximum optimization
• FFI: fusion-python crate provides PyO3 bindings producing scpn_fusion_rs.so/.pyd
• Dependencies: ndarray, nalgebra, rayon (parallelism), rustfft, serde
• No external runtime: pure Rust with no C/Fortran dependencies

Documentation

• Compact Reactor Findings
• Physics Methods
• ITER Validation
• Neuro-Symbolic Compiler Architecture
• Packet C Control API
• Future Applications
• Comprehensive Technical Study (30,000+ words)

Citation

If you use SCPN Fusion Core in your research, please cite using the CITATION.cff file or:

@software{scpn_fusion_core,
  title   = {SCPN Fusion Core: Tokamak Plasma Physics Simulation and Neuro-Symbolic Control Suite},
  author  = {Sotek, Miroslav and Reiprich, Michal},
  year    = {2026},
  url     = {https://github.com/anulum/scpn-fusion-core},
  version = {1.0.0}
}

This software is archived on Zenodo (DOI pending first release deposit) and published on Academia.edu.

Authors

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

License

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

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