dataset card

README.md

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+---
+license: cc-by-4.0
+tags: [plasma-physics, gyrokinetics, turbulence, scientific-data]
+---
+
+# Gyrokinetic adiabatic-electron turbulence (256 trajectories)
+
+Adiabatic-electron gyrokinetic turbulence simulations (GKW): the full 5D distribution
+function and electrostatic potentials at every timestep, in bfloat16. This is the
+dataset used to train the GyroSwin neural surrogates.
+
+- GyroSwin (surrogate model, source of this dataset): https://arxiv.org/abs/2510.07314
+- PINC (physics-informed neural compression): https://arxiv.org/abs/2602.04758v2
+- Code: https://github.com/ml-jku/neural-gyrokinetics
+
+## Parameter scan
+
+Cyclone Base Case (CBC) ion-temperature-gradient turbulence, scanned across the trajectories:
+
+| Parameter | Range |
+|---|---|
+| ion temperature gradient | 3.71 – 11.97 |
+| density gradient | 0.00 – 6.99 |
+| magnetic shear (ŝ) | 0.51 – 5.00 |
+| safety factor (q) | 1.55 – 8.99 |
+
+Grid resolution `(nvpar, nmu, ns, nkx, nky) = (32, 8, 16, 85, 32)`, fixed across trajectories.
+
+## Storage precision (bf16)
+
+Data is stored in bfloat16. Reconstruction loss vs float32, over ~4900 random snapshots across all trajectories:
+
+| quantity | mean | worst |
+|---|---|---|
+| 5D field PSNR ↑ | 85.5 | 72.7 |
+| 5D field rel. L2 ↓ | 1.66e-3 | 1.69e-3 |
+| heat-flux rel. L1 ↓ | 7.0e-6 | 2.1e-4 |
+| potential rel. L1 ↓ | 2.2e-4 | 4.6e-3 |
+
+## Structure
+
+```
+iteration_<n>_ifft_realpotens/
+  data/timestep_<t>.bf16.bin   # 5D distribution function f
+  data/poten_<t>.bf16.bin      # electrostatic potentials
+  metadata_light.npz           # geometry, grid, spectra metadata
+  input.dat                    # GKW input deck
+```
+
+## Usage
+
+Download the dataset (the full set is large; grab **one trajectory** first to get started):
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    "gerkone/cbc-gyroswin-256traj",
+    repo_type="dataset",
+    allow_patterns=["iteration_0_ifft_realpotens/*"],   # just ONE trajectory (~12 GB); drop this line for the full set
+    local_dir="data/preprocessed_kvikio",
+)
+```
+
+Train GyroSwin, from the [code repo](https://github.com/ml-jku/neural-gyrokinetics):
+
+```bash
+python main.py \
+  workflow=gyroswin dataset=cyclone_gyroswin model=multi \
+  dataset.path=data/preprocessed_kvikio \
+  +dataset.prefer_dtype=bf16 \
+  training.batch_size=1
+```
+
+Notes:
+- **`+dataset.prefer_dtype=bf16` is required** — this dataset ships bf16 shards (`*.bf16.bin`); without it the loader looks for fp32 shards and errors.
+- The training command uses the dataset config's **full** trajectory set — download all trajectories first (drop the `allow_patterns` filter above). To just smoke-test on the single pulled trajectory, add `dataset.training_trajectories=[iteration_0] dataset.validation_trajectories=[iteration_0]` (trajectory names omit the `_ifft_realpotens` suffix).
+- `df` normalization is recomputed from the field data on first load (a stats cache is written next to the data); the precomputed stats are intentionally not shipped.
+- Both training and evaluation run directly on the bf16 shards (`prefer_dtype=bf16` applies to the validation loader too).