AGNI: A differentiable MHD stability solver & optimizer for magnetic confinement fusion devices

[2608.01750] AGNI: A differentiable MHD stability solver & optimizer for magnetic confinement fusion devices Skip to main content Search Submit Donate Log in Physics > Plasma Physics arXiv:2608.01750 (physics) [Submitted on 3 Aug 2026 ( v1 ), last revised 30 Sep 2026 (this version, v3)] Title: AGNI: A differentiable MHD stability solver & optimizer for magnetic confinement fusion devices Authors: Rahul Gaur , Sanket Patil , Prateek Gupta , Djin Patch , Tony Qian View PDF HTML (experimental) Abstract: The existence of an ideal MagnetoHydroDynamic (MHD) equilibrium does not guarantee its stability. Finite toroidal mode number (n) instabilities degrade performance in both tokamaks and stellarators and differentiable stability optimization tools to date have operated only in the infinite-n limit. We present AGNI (Analysis of Global Normal modes in Ideal MHD), a GPU-accelerated, automatically differentiable finite-n ideal MHD stability solver and optimizer. AGNI discretizes the ideal MHD energy principle pseudospectrally in real space using differentiation matrices and geometric coefficients from a DESC equilibrium, giving a variational eigenvalue problem for the plasma displacement, and efficiently finds the most unstable modes. Built on jax, AGNI yields reverse-mode gradients of the growth rate with respect to boundary-shape and profile parameters without re-solving the equilibrium. We benchmark AGNI against the initial-value code NIMSTELL for a modified Landreman-Bulle-Drevlak quasi-helically symmetric stellarator and a DSHAPE tokamak,recovering the dominant mode with agreement in terms of growth rate and eigenfunction structure, and verify the automatic differentiation gradients against central finite differences. We quantify CPU and GPU cost for eigenvalue and gradient evaluation, establish the finite-precision limit on resolving near-marginal eigenvalues, and present a numerical scheme to impose incompressibility, compatible with gradient-based optimization. AGNI will allow us to optimize tokamaks, stellarators, and mirrors against ideal MHD instabilities. Comments: 28 pages, 8 figures, added AI use declaration, link to source code, improved readability Subjects: Plasma Physics (physics.plasm-ph) Cite as: arXiv:2608.01750 [physics.plasm-ph] (or arXiv:2608.01750v3 [physics.plasm-ph] for this version) https://doi.org/10.48550/arXiv.2608.01750 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rahul Gaur [ view email ] [v1] Mon, 3 Aug 2026 06:16:49 UTC (2,220 KB) [v2] Thu, 27 Aug 2026 02:56:56 UTC (1,978 KB) [v3] Wed, 30 Sep 2026 01:06:38 UTC (8,452 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.plasm-ph < prev | next > new | recent | 2026-08 Change to browse by: physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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