SCPN Control: Neuro-Symbolic Stochastic Petri Net Controller for Plasma Control

Standalone neuro-symbolic control engine that compiles Stochastic Petri Nets into spiking neural network controllers with formal verification guarantees. Features real-time adaptive Kuramoto coupling driven by tokamak diagnostics (beta, disruption risk, Mirnov, coherence PI), Lyapunov stability guard, H-infinity observer, plasma-native 8-layer Knm hierarchy, and WebSocket phase streaming. 52 Python modules, 5 Rust crates, 1888 tests.

Part of the SCPN (Self-Consistent Phenomenological Network) research framework by ANULUM. v0.4.0 introduces real-time adaptive Knm coupling driven by tokamak diagnostics — the first Kuramoto phase dynamics engine with online-adaptive coupling from plasma state.

Keywords

neuro-symbolic AI, stochastic Petri net, adaptive coupling, disruption prediction, plasma physics, H-infinity control, Lyapunov stability, Kuramoto oscillators, phase dynamics, tokamak control, spiking neural network, SCPN

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