This upload presents the ALADIN-G₂ computational framework, a GPU-accelerated numerical solver for magnetohydrodynamics (MHD) implemented in CuPy and CUDA. The code extends a standard GLM-MHD formulation with additional auxiliary fields inspired by octonion algebra and G₂-structured symbolic interactions. The solver includes: A 3D GLM-MHD evolution system with divergence cleaning (ψ-field) GPU-accelerated volume operator kernel (CUDA RawKernel) High-order time integration (SSP-RK3) Positivity-preserving and stabilization limiters Auxiliary “octonion-inspired” fields for structured nonlinear coupling Optional entropy-motivated dissipation and variational regularization terms The goal of this framework is to explore alternative structured regularization strategies in plasma simulation, rather than to propose a physically complete or experimentally validated model. Content This repository includes: Python/CuPy implementation of the solver CUDA kernel for volume operator computation Octonion-inspired algebraic utilities Time-stepping and stabilization routines Example configuration for small-scale simulations Methodological Notes The formulation combines: Classical ideal MHD conservation laws GLM divergence cleaning Artificial dissipation for numerical stability Additional nonlinear correction terms based on: scalar 3-form φ(O) associator norm |O,O,O| gradient-like penalty terms (optional) These additions are numerical and structural in nature, and should not be interpreted as a first-principles physical theory of G₂ geometry or octonion physics. Intended Use This code is intended for: Experimental numerical physics research GPU kernel design exploration Testing stability effects of algebraic augmentation in PDE systems Educational and prototyping purposes in computational MHD It is not intended as a production-grade astrophysical solver or validated physical model. Requirements Python ≥ 3.10 CuPy (CUDA-compatible GPU required) NumPy NVIDIA GPU (tested on A100-class hardware) Notes on Reproducibility This implementation is a research prototype. Results may depend on: grid resolution (Ni) stabilization parameters (α, λ) boundary conditions GPU architecture Users are encouraged to treat outputs as qualitative behavior experiments rather than physical predictions. License MIT (code reuse allowed)
Mihai Alexandru Bucurenciu (Sat,) studied this question.