docs: Update README.md

README.md

@@ -1,3 +1,49 @@
 ---
 license: mit
+tags:
+- physics
+- fusion
+- ml
+- ai4science
+pretty_name: Near-Axis Stellarators Dataset
+size_categories:
+- 1M<n<10M
 ---
+# 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}}
+```