Every output is cited at source. Each publication below links to its DOI, open-access deposit, preprint, and the evidence bundles that bound its claims.
A domain-agnostic coherence control compiler built on Kuramoto/UPDE phase dynamics. Features a 3-channel oscillator model (Physical/Informational/Symbolic), coupling matrix management with exponential decay, regime-aware supervision, and a Rust kernel (spo-kernel) with PyO3 FFI bindings.
A high-performance neuromorphic computing framework combining stochastic computing with spiking neural networks. 122 neuron models (1943-2026), Rust SIMD engine (111 models, 41.3 Gbit/s AVX-512, PyO3 bindings), bit-true Verilog RTL co-simulation, FPGA synthesis via IR compiler (SystemVerilog + MLIR/CIRCT), equation-to-Verilog compiler, formal verification (SymbiYosys, 7 modules, 65 properties), NIR bridge (18/18 primitives, interop with Norse/snnTorch/SpikingJelly), quantum hybrid computing (Qiskit + PennyLane), hyper-dimensional computing (HDC/VSA), Petri net simulation, CuPy GPU acceleration, JAX JIT training, MPI distributed simulation, identity continuity substrate, and 125-function spike train analysis toolkit.
SCPN-MIF-CORE provides deterministic phase synchronisation and hardware synthesis for high-beta pulsed plasmas, targeting sub-50-nanosecond combinatorial sensor-to-actuator triggering on AMD Xilinx UltraScale+ FPGAs. It discards steady-state Grad-Shafranov logic in favour of rigid-rotor field-reversed-configuration equilibrium and two-fluid Hall-MHD with non-adiabatic flux evolution, and compiles Kuramoto kinematic merging models into bit-true Q8.8 SystemVerilog through the sibling sc-neurocore engine. The framework carries a multi-language compute chain (Python reference, Rust runtime, Julia/Go polyglot-parity references), 100% test coverage, libFuzzer harnesses for untrusted-input surfaces, and Lean 4 formal-verification gates.
This software-framework note archives scpn-quantum-control , a reproducible Kuramoto--XY quantum-control workflow combining Python/Qiskit orchestration, Rust acceleration, hardware artefact packaging, and publication-grade analysis surfaces. The software supports construction of heterogeneous oscillator networks, XY-Hamiltonian mapping, topology-informed ansatz generation, simulator and hardware execution, and reproducible post-processing of IBM artefacts. The record documents the framework as a research software artefact rather than a standalone physics claim. It highlights the repository structure, reproducibility workflow, accelerated kernels, benchmark and parity checks, and the relationship between source code, generated artefacts, and paper records. The archive includes the JOSS-style manuscript source, bibliography, preview TeX, and rebuilt PDF. Repository field and DOI metadata link the software note back to https://github.com/anulum/scpn-quantum-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.
A comprehensive tokamak plasma physics simulation and control suite with 54 Python modules, 10 Rust crates, 26 simulation modes, and a neuro-symbolic Petri net to stochastic neuron compiler. Features Grad-Shafranov equilibrium, MHD stability, transport, RF heating, neutronics, disruption prediction, and the MVR-0.96 compact reactor optimizer with optional SC-NeuroCore SNN integration.