SCPN-Control

Neuro-symbolic control engine that compiles Stochastic Petri Nets into spiking neural network controllers. Five-tier gyrokinetic transport, 10 controller types, Grad-Shafranov equilibrium, JAX-differentiable.

PyPI Package 10 Controllers Physics Solvers
196Source Modules
64Rust Modules
546Test Files
10Controller Types
11.9 µsP50 Kernel Step
5GK Transport Tiers
155Validation Scripts
33Equations Audited

Install

# Core (numpy, scipy, click)
pip install scpn-control

# With dashboard + JAX + machine learning
pip install "scpn-control[dashboard,jax,ml]"

# Everything
pip install "scpn-control[all]"

Core Pipeline

Petri Net
SPN structure
→
SNN Compiler
LIF + bitstream
→
Contracts
Pre/post checks
→
Controller
10 types
→
Plant Model
GS + transport

Key Capabilities

SPN → SNN Compilation
Stochastic Petri Nets compiled into LIF neurons with bitstream encoding. Contract-based pre/post-condition checking at runtime.
Grad-Shafranov Equilibrium
Fixed + free-boundary solver. Jacobi, SOR, multigrid. JAX-differentiable. Neural PCA+MLP surrogate (1000× speedup).
5-Tier Gyrokinetic Transport
Critical-gradient, QLKNN surrogate, native linear eigenvalue, native TGLF-equivalent (SAT0/SAT1/SAT2), nonlinear δf GK (5D Vlasov).
10 Controller Types
PID, MPC, NMPC, H∞, μ-synthesis, gain-scheduled, sliding-mode, fault-tolerant, SNN, PPO reinforcement learning.
Rust Acceleration
5 crates, 64 modules, 23,734 LOC. PyO3 bindings. Criterion-benchmarked: 11.9 µs P50 kernel step. Up to 2.5× speedup at scale.
JAX Autodiff
JAX-differentiable GS solver, transport, neural equilibrium. JIT compilation. GPU support. Gradient through complete equilibrium solve.

Explore

Physics Solvers →
GS equilibrium, MHD stability, transport, gyrokinetics, edge/pedestal, neoclassical.
10 Controllers →
PID to PPO. H∞, NMPC, sliding-mode, fault-tolerant, SNN compilation.
Phase Dynamics →
Paper 27 Kuramoto/UPDE. 16×16 Knm, PAC gating, Lyapunov guard.
196 Modules →
Current v0.23.0 source inventory across the control, physics, phase, and operational surfaces.
Benchmarks →
Criterion, Kuramoto scaling, GK linear, PPO training results.
9 Notebooks →
Phase dynamics, Q10, SNN compiler, H∞, frontier physics, digital twin.

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Project resources

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