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SCPN Fusion Core — Advanced Plasma Physics & Neuromorphic Control Suite for Tokamak Reactors

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SCPN Fusion Core – Tokamak plasma SIM and neuromorphic SNN control

by anulum·Feb 12, 2026·2 points·0 comments
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AI Analysis

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The Take

This repo compiles stochastic Petri-net control policies into sub-millisecond spiking LIF networks and pairs them with reduced-order plasma simulators and Rust acceleration — not something you see every day. It ships validation against real equilibria/shot databases and a Streamlit dashboard, so the project feels like a serious research-to-prototype pipeline rather than paper-only ideas; the tradeoff is deliberate reduced-order physics (not a TRANSP/GENE replacement), which is fair for real-time control work.

Category
Open Source
Target Audience

Plasma physicists, fusion control engineers, neuromorphic/SNN researchers, and developers building real-time control systems for tokamaks

Post Description

SCPN Fusion Core is an open-source Python/Rust suite for tokamak plasma simulation with neuro-symbolic compilation to stochastic spiking neural networks for real-time, fault-tolerant control.

Key features: - 26 simulation modes (equilibrium, transport, optimizer, flight simulator, neuro-control, etc.) - Neuro-symbolic compiler: Petri nets → stochastic LIF neurons (sub-ms latency, 40%+ bit-flip resilience) - Validation: SPARC high-field equilibria + ITPA H-mode database (20 entries, 10 machines) + IPB98(y,2) scaling - Multigrid solvers, property-based testing, Rust acceleration, Streamlit dashboard - Install: pip install scpn-fusion

GitHub: https://github.com/anulum/scpn-fusion-core

Built to explore neuromorphic approaches to fusion reactor control. Happy to answer questions about the models, compiler, validation, or performance.

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I just shipped the canonical neuro-symbolic control demo

Compiles stochastic Petri nets into a verified SNN (LIF + bitstream) and executes closed-loop replay on real DIII‑D shot data with liveness/boundedness proofs and SHA256 proof bundles. The notebook also ships side-by-side SNN vs PID vs MPC metrics, a FusionKernel digital twin, and deterministic artifact export — impressive technical depth for experimental fusion control, but clearly aimed at specialists and not turnkey for newcomers.

WizardryNiche Gem
anulum
103mo ago