pedrocurvo/near-axis-stellarators

NearAxisStellarators

The NearAxisStellarators dataset contains stellarator configurations generated with the 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).

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📊 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.

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🛠️ 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
• Code: MLStellaratorDesign

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📖 Citation

If you use this dataset, please cite:

@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}}