---
license: mit
tags:
- physics
- fusion
- ml
- ai4science
pretty_name: Near-Axis Stellarators Dataset
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: good
    path: "good-stels.csv"
  - split: viable
    path: "viable-stels.csv"
---
# NearAxisStellarators

The **NearAxisStellarators** dataset contains stellarator configurations generated with the [pyQSC](https://github.com/landreman/pyQSC) near-axis expansion code.  
It provides **input design parameters** (magnetic axis Fourier coefficients, field strength coefficients, number of field periods, pressure) and the resulting **plasma properties** (rotational transform, elongation, Mercier stability, quasisymmetry, etc.).

This dataset supports both **forward modeling** (parameters → properties) and **inverse design** (desired properties → candidate parameters). 

---
# 📊 Applications

- Plasma physics research – explore distributions of stable stellarators.
- Machine learning – benchmark regression, density estimation, or inverse design models.
- Fusion design optimization – generate candidate stellarators with desired confinement properties.

---
# 🛠️ Dataset Generation
1.	Sampled design parameters from uniform distributions in physically relevant ranges.
2.	Evaluated equilibrium properties with pyQSC near-axis expansion.
3.	Applied physical constraints (Mercier stability, finite β, elongation, quasisymmetry).
4.	Iteratively retrained a Mixture Density Network (MDN) to enrich the dataset with “good” stellarators, increasing success rate from ~0.002% (random) to ~20%.

For details, see the paper and the code:
- Paper: [Cambridge Press PDF](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/BF500E9C52CE233B3CB5320F37AA1813/S002237782400165Xa.pdf/using_deep_learning_to_design_high_aspect_ratio_fusion_devices.pdf)
- Code: [MLStellaratorDesign](https://github.com/pedrocurvo/MLStellaratorDesign)

---
# 📖 Citation

If you use this dataset, please cite:
```bash
@article{Curvo2025,
          title={Using deep learning to design high aspect ratio fusion devices},
          volume={91}, DOI={10.1017/S002237782400165X}, number={1},
          journal={Journal of Plasma Physics},
          author={Curvo, P. and Ferreira, D.R. and Jorge, R.},
          year={2025},
          pages={E38}}
```