# A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations

arXiv: https://arxiv.org/abs/2603.15240 · HTML: https://mlsanity.com/paper/2603.15240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 58 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [The DESC Stellarator Code Suite Part II: Perturbation and continuation methods](https://mlsanity.com/paper/2203.15927/) (2022)
- [The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization](https://mlsanity.com/paper/2204.00078/) (2022)
- [Single-stage gradient-based stellarator coil design: stochastic optimization](https://mlsanity.com/paper/2106.12137/) (2021)

## Abstract (arXiv)

Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations.