agnimhd

AGNI is a differentiable finite-n ideal MHD stability solver. It discretizes the ideal MHD energy principle on a straight-field-line grid and returns the squared growth rate of the most unstable mode (positive: unstable) and its gradient. It computes the stability objective; it does not run an optimization itself.

The package is under active development, and the API, the file format and the numerics change without notice. For a stable version use the AGNI implementation inside DESC, PR #1893, branch rg/AGNI_var.

Quick start

Stability of a DESC equilibrium (needs DESC installed):

agnimhd solve my_equilibrium.h5 --res 24,12,8

prints gamma^2 for each toroidal mode family n = x + k NFP, all solved on one field period, and names the most unstable.

From Python:

import agnimhd

basis = agnimhd.Basis(24, 12, 8)                 # n_zeta = 8 per field period
eq, diffmat = agnimhd.from_desc("my_equilibrium.h5", basis, family=0)
gamma2 = agnimhd.growth_rate(eq, diffmat)        # gamma^2 = -lambda > 0: unstable

growth_rate is solve mode and is not differentiable. The gradient comes from optimize mode, jax.grad(agnimhd.growth_rate_of)(params, equilibrium_map, diffmat), with equilibrium_map the equilibrium code's map from its parameters to an EquilibriumData.

Matrices too large for one GPU: SolverConfig(eigensolver="dense_mg") splits the dense matrix over all GPUs of a node with JAXMg (tested with jaxmg==0.0.9); 182,784 unknowns in 8.5 minutes on four A100s. See the multi-GPU page of the documentation.

Two shipped cases run without DESC: python examples/cross_sections.py. Other codes build an EquilibriumData from arrays, or load a saved one with EquilibriumData.load("eq.npz"). See the documentation.

Install

pip install agnimhd               # jax, numpy, scipy, matfree
pip install "agnimhd[hdf5,test]"  # HDF5 files and the test suite

Python 3.12. DESC is optional and only needed for from_desc. GPU, DESC and multi-GPU set-ups: Installation.

Development

pip install -e ".[dev]"
pre-commit install
pytest tests -q          # about 25 min on one CPU

Releasing: tag vX.Y.Z on master and publish a GitHub release of it. The release workflow builds the package from the tag and uploads it to PyPI, and Zenodo archives the release with a DOI. The version comes from the tag (setuptools_scm); nothing in the source is edited.

Reference

R. Gaur, S. Patil, P. Gupta, D. Patch, T. Qian, AGNI: A differentiable MHD stability solver & optimizer for magnetic confinement fusion devices, arXiv:2608.01750 (2026), doi:10.48550/arXiv.2608.01750. PDF of v3 in this repository: docs/paper/agni_arxiv_2608.01750v3.pdf.

@article{gaur2026agni,
  title   = {{AGNI}: A differentiable {MHD} stability solver \& optimizer for magnetic confinement fusion devices},
  author  = {Gaur, Rahul and Patil, Sanket and Gupta, Prateek and Patch, Djin and Qian, Tony},
  journal = {arXiv preprint arXiv:2608.01750},
  year    = {2026},
  doi     = {10.48550/arXiv.2608.01750}
}

Originally developed inside DESC (PR #1893, on the differentiation matrices of PR #1789). MIT license.