# Using Deep Learning to Design High Aspect Ratio Fusion Devices

arXiv: https://arxiv.org/abs/2409.00564 · HTML: https://mlsanity.com/paper/2409.00564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)

## Built on by (most cited first, 3 of 3)

- [How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning](https://mlsanity.com/paper/2502.11657/) (2025)
- [ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks](https://mlsanity.com/paper/2506.19583/) (2025)
- [Diffusion for Fusion: Designing Stellarators with Generative AI](https://mlsanity.com/paper/2511.20445/) (2025)

## Abstract (arXiv)

The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite plasma beta, and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favorable confinement properties by finding a solution to the inverse design problem, that is, obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method.