Dataset Viewer (First 5GB)
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metrics.id
string
metrics.json
string
metrics.aspect_ratio
float64
metrics.aspect_ratio_over_edge_rotational_transform
float64
metrics.average_triangularity
float64
metrics.axis_magnetic_mirror_ratio
float64
metrics.axis_rotational_transform_over_n_field_periods
float64
metrics.edge_magnetic_mirror_ratio
float64
metrics.edge_rotational_transform_over_n_field_periods
float64
metrics.flux_compression_in_regions_of_bad_curvature
float64
metrics.max_elongation
float64
metrics.minimum_normalized_magnetic_gradient_scale_length
float64
metrics.qi
float64
metrics.vacuum_well
float64
boundary.json
string
boundary.is_stellarator_symmetric
bool
boundary.n_field_periods
int64
boundary.r_cos
sequence
boundary.r_sin
null
boundary.z_cos
null
boundary.z_sin
sequence
omnigenous_field_and_targets.id
string
omnigenous_field_and_targets.json
string
omnigenous_field_and_targets.aspect_ratio
float64
omnigenous_field_and_targets.major_radius
float64
omnigenous_field_and_targets.max_elongation
float64
omnigenous_field_and_targets.omnigenous_field.modB_spline_knot_coefficients
sequence
omnigenous_field_and_targets.omnigenous_field.n_field_periods
int64
omnigenous_field_and_targets.omnigenous_field.poloidal_winding
int64
omnigenous_field_and_targets.omnigenous_field.torodial_winding
int64
omnigenous_field_and_targets.omnigenous_field.x_lmn
sequence
omnigenous_field_and_targets.rotational_transform
float64
desc_omnigenous_field_optimization_settings.id
string
desc_omnigenous_field_optimization_settings.json
string
desc_omnigenous_field_optimization_settings.equilibrium_settings.check_orientation
bool
desc_omnigenous_field_optimization_settings.equilibrium_settings.max_poloidal_mode
int64
desc_omnigenous_field_optimization_settings.equilibrium_settings.max_toroidal_mode
int64
desc_omnigenous_field_optimization_settings.equilibrium_settings.psi
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.aspect_ratio
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.elongation
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.is_iota_positive
bool
desc_omnigenous_field_optimization_settings.initial_guess_settings.is_stellarator_symmetric
bool
desc_omnigenous_field_optimization_settings.initial_guess_settings.major_radius
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.max_elongation
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.max_poloidal_mode
int64
desc_omnigenous_field_optimization_settings.initial_guess_settings.max_toroidal_mode
int64
desc_omnigenous_field_optimization_settings.initial_guess_settings.mirror_ratio
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.n_field_periods
int64
desc_omnigenous_field_optimization_settings.initial_guess_settings.rotational_transform
float64
desc_omnigenous_field_optimization_settings.initial_guess_settings.torsion
float64
desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.name
string
desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.target_kind
string
desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.weight
float64
desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.name
string
desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.target_kind
string
desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.weight
float64
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_M_factor
int64
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_N_factor
int64
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_rho
float64
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eta_weight
float64
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.name
string
desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.weight
float64
desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.name
string
desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.target_kind
string
desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.weight
float64
desc_omnigenous_field_optimization_settings.optimizer_settings.maxiter
int64
desc_omnigenous_field_optimization_settings.optimizer_settings.name
string
desc_omnigenous_field_optimization_settings.optimizer_settings.verbose
int64
vmec_omnigenous_field_optimization_settings.id
string
vmec_omnigenous_field_optimization_settings.json
string
vmec_omnigenous_field_optimization_settings.gradient_based_relative_objectives_tolerance
float64
vmec_omnigenous_field_optimization_settings.gradient_free_budget_per_design_variable
int64
vmec_omnigenous_field_optimization_settings.gradient_free_max_time
int64
vmec_omnigenous_field_optimization_settings.gradient_free_optimization_hypercube_bounds
float64
vmec_omnigenous_field_optimization_settings.infinity_norm_spectrum_scaling
float64
vmec_omnigenous_field_optimization_settings.max_poloidal_mode
int64
vmec_omnigenous_field_optimization_settings.max_toroidal_mode
int64
vmec_omnigenous_field_optimization_settings.n_inner_optimizations
int64
vmec_omnigenous_field_optimization_settings.use_continuation_method_in_fourier_space
bool
vmec_omnigenous_field_optimization_settings.verbose
bool
qp_init_omnigenous_field_optimization_settings.id
string
qp_init_omnigenous_field_optimization_settings.json
string
qp_init_omnigenous_field_optimization_settings.aspect_ratio
float64
qp_init_omnigenous_field_optimization_settings.elongation
float64
qp_init_omnigenous_field_optimization_settings.is_iota_positive
bool
qp_init_omnigenous_field_optimization_settings.is_stellarator_symmetric
bool
qp_init_omnigenous_field_optimization_settings.major_radius
float64
qp_init_omnigenous_field_optimization_settings.mirror_ratio
float64
qp_init_omnigenous_field_optimization_settings.n_field_periods
int64
qp_init_omnigenous_field_optimization_settings.torsion
float64
nae_init_omnigenous_field_optimization_settings.id
string
nae_init_omnigenous_field_optimization_settings.json
string
nae_init_omnigenous_field_optimization_settings.aspect_ratio
float64
nae_init_omnigenous_field_optimization_settings.max_elongation
float64
nae_init_omnigenous_field_optimization_settings.max_poloidal_mode
int64
nae_init_omnigenous_field_optimization_settings.max_toroidal_mode
int64
nae_init_omnigenous_field_optimization_settings.mirror_ratio
float64
nae_init_omnigenous_field_optimization_settings.n_field_periods
int64
nae_init_omnigenous_field_optimization_settings.rotational_transform
float64
misc.vmecpp_wout_id
string
misc.has_optimize_boundary_omnigenity_vmec_error
bool
misc.has_optimize_boundary_omnigenity_desc_error
bool
misc.has_generate_qp_initialization_from_targets_error
bool
misc.has_generate_nae_initialization_from_targets_error
bool
misc.has_neurips_2025_forward_model_error
bool
plasma_config_id
string
DfpsEPzxXHTUgveVPNFVxyw
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End of preview. Expand in Data Studio

Dataset Card for ConStellaration

A dataset of diverse quasi-isodynamic (QI) stellarator boundary shapes with corresponding performance metrics and ideal magneto-hydrodynamic (MHD) equilibria, as well as settings for their generation.

Dataset Details

Dataset Description

Stellarators are magnetic confinement devices that are being pursued to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained optimization problem that requires expensive physics simulations and significant domain expertise. Specifically, QI-stellarators are seen as a promising path to commercial fusion due to their intrinsic avoidance of current-driven disruptions. With the release of this dataset, we aim to lower the barrier for optimization and machine learning researchers to contribute to stellarator design, and to accelerate cross-disciplinary progress toward bringing fusion energy to the grid.

  • Curated by: Proxima Fusion
  • License: MIT

Diagram of the computation of metrics of interest from a plasma boundary via the MHD equilibrium

Dataset Sources

Dataset Structure

The dataset consists of 2 tabular parts. Both parts have a column plasma_config_id in common which can be used to associate respective entries:

default vmecpp_wout
Contains information about:
  • Plasma boundaries
  • Ideal MHD metrics
  • Omnigenous field and targets, used as input for sampling of plasma boundaries
  • Sampling settings for various methods (DESC, VMEC, QP initialization, Near-axis expansion)
  • Miscellaneous information about errors that might have occurred during sampling or metrics computation.
For each of the components above there is an identifier column (ending with `.id`), a JSON column containing a JSON-string representation, as well as one column per leaf in the nested JSON structure (with `.` separating the keys on the JSON path to the respective leaf).
Contains, for each plasma boundary, a JSON-string representations of the "WOut" file as obtained when running VMEC, initialized on the boundary.
The JSON representation can be converted to a VMEC2000 output file.

The columns plasma_config_id and vmecpp_wout_id are present in both parts and link the two in both directions.

Uses

Install Huggingface Datasets: pip install datasets

Basic Usage

Load the dataset and convert to a Pandas Dataframe (here, torch is used as an example; install it with" pip install torch):

import datasets
import torch
from pprint import pprint

ds = datasets.load_dataset(
    "proxima-fusion/constellaration",
    split="train",
    num_proc=4,
)
ds = ds.select_columns([c for c in ds.column_names
                        if c.startswith("boundary.")
                        or c.startswith("metrics.")])
ds = ds.filter(
    lambda x: x == 3,
    input_columns=["boundary.n_field_periods"],
    num_proc=4,
)
ml_ds = ds.remove_columns([
    "boundary.n_field_periods", "boundary.is_stellarator_symmetric",  # all same value
    "boundary.r_sin", "boundary.z_cos",  # empty
    "boundary.json", "metrics.json", "metrics.id",  # not needed
])

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch_ds = ml_ds.with_format("torch", device=device)  # other options: "jax", "tensorflow" etc.

for batch in torch.utils.data.DataLoader(torch_ds, batch_size=4, num_workers=4):
    pprint(batch)
    break
Output
{'boundary.r_cos': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
          -6.5763e-02, -3.8500e-02,  2.2178e-03,  4.6007e-04],
         [-6.6648e-04, -1.0976e-02,  5.6475e-02,  1.4193e-02,  8.3476e-02,
          -4.6767e-02, -1.3679e-02,  3.9562e-03,  1.0087e-04],
         [-3.5474e-04,  4.7144e-03,  8.3967e-04, -1.9705e-02, -9.4592e-03,
          -5.8859e-03,  1.0172e-03,  9.2020e-04, -2.0059e-04],
         [ 2.9056e-03,  1.6125e-04, -4.0626e-04, -8.0189e-03,  1.3228e-03,
          -5.3636e-04, -7.3536e-04,  3.4558e-05,  1.4845e-04],
         [-1.2475e-04, -4.9942e-04, -2.6091e-04, -5.6161e-04,  8.3187e-05,
          -1.2714e-04, -2.1174e-04,  4.1940e-06, -4.5643e-05]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  9.9909e-01,
          -6.8512e-02, -8.1567e-02,  2.5140e-02, -2.4035e-03],
         [-3.4328e-03,  1.6768e-02,  1.2305e-02, -3.6708e-02,  1.0285e-01,
           1.1224e-02, -2.3418e-02, -5.4137e-04,  9.3986e-04],
         [-2.8389e-03,  1.4652e-03,  1.0112e-03,  9.8102e-04, -2.3162e-02,
          -6.1180e-03,  1.5327e-03,  9.4122e-04, -1.2781e-03],
         [ 3.9240e-04, -2.3131e-04,  4.5690e-04, -3.8244e-03, -1.5314e-03,
           1.8863e-03,  1.1882e-03, -5.2338e-04,  2.6766e-04],
         [-2.8441e-04, -3.4162e-04,  5.4013e-05,  7.4252e-04,  4.9895e-04,
          -6.1110e-04, -8.7185e-04, -1.1714e-04,  9.9285e-08]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
           6.9176e-02, -1.8489e-02, -6.5094e-03, -7.6238e-04],
         [ 1.4062e-03,  4.2645e-03, -1.0647e-02, -8.1579e-02,  1.0522e-01,
           1.6914e-02,  6.5321e-04,  6.9397e-04,  2.0881e-04],
         [-6.5155e-05, -1.2232e-03, -3.3660e-03,  9.8742e-03, -1.4611e-02,
           6.0985e-03,  9.5693e-04, -1.0049e-04,  5.4173e-05],
         [-4.3969e-04, -5.1155e-04,  6.9611e-03, -2.8698e-04, -5.8589e-03,
          -5.4844e-05, -7.3797e-04, -5.4401e-06, -3.3698e-05],
         [-1.9741e-04,  1.0003e-04, -2.0176e-04,  4.9546e-04, -1.6201e-04,
          -1.9169e-04, -3.9886e-04,  3.3773e-05, -3.5972e-05]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00,
           1.1652e-01, -1.5593e-02, -1.0215e-02, -1.8656e-03],
         [ 3.1697e-03,  2.1618e-02,  2.7072e-02, -2.4032e-02,  8.6125e-02,
          -7.1168e-04, -1.2433e-02, -2.0902e-03,  1.5868e-04],
         [-2.3877e-04, -4.9871e-03, -2.4145e-02, -2.1623e-02, -3.1477e-02,
          -8.3460e-03, -8.8675e-04, -5.3290e-04, -2.2784e-04],
         [-1.0006e-03,  2.1055e-05, -1.7186e-03, -5.2886e-03,  4.5186e-03,
          -1.1530e-03,  6.2732e-05,  1.4212e-04,  4.3367e-05],
         [ 7.8993e-05, -3.9503e-04,  1.5458e-03, -4.9707e-04, -3.9470e-04,
           6.0808e-04, -3.6447e-04,  1.2936e-04,  6.3461e-07]]]),
 'boundary.z_sin': tensor([[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
          -1.4295e-02,  1.4929e-02, -6.6461e-03, -3.0652e-04],
         [ 9.6958e-05, -1.6067e-03,  5.7568e-02, -2.2848e-02, -1.6101e-01,
           1.6560e-02,  1.5032e-02, -1.2463e-03, -4.0128e-04],
         [-9.9541e-04,  3.6108e-03, -1.1401e-02, -1.8894e-02, -7.7459e-04,
           9.4527e-03, -4.6871e-04, -5.5180e-04,  3.2248e-04],
         [ 2.3465e-03, -2.4885e-03, -8.4212e-03,  8.9649e-03, -1.9880e-03,
          -1.6269e-03,  8.4700e-04,  3.7171e-04, -6.8400e-05],
         [-3.6228e-04, -1.8575e-04,  6.0890e-04,  5.0270e-04, -6.9953e-04,
          -7.6356e-05,  2.3796e-04, -3.2524e-05,  5.3396e-05]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
          -8.5341e-02,  2.4825e-02,  8.0996e-03, -7.1501e-03],
         [-1.3470e-03,  4.6367e-03,  4.1579e-02, -3.6802e-02, -1.5076e-01,
           7.1852e-02, -1.9793e-02,  8.2575e-03, -3.8958e-03],
         [-2.3956e-03, -5.7497e-03,  5.8264e-03,  9.4471e-03, -3.5171e-03,
          -1.0481e-02, -3.2885e-03,  4.0624e-03,  4.3130e-04],
         [ 6.3403e-05, -9.2162e-04, -2.4765e-03,  5.4090e-04,  1.9999e-03,
          -1.1500e-03,  2.7581e-03, -5.7271e-04,  3.0363e-04],
         [ 4.6278e-04,  4.3696e-04,  8.0524e-05, -2.4660e-04, -2.3747e-04,
           5.5060e-05, -1.3221e-04, -5.4823e-05,  1.6025e-04]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
          -1.6090e-01, -1.4364e-02,  3.7923e-03,  1.8234e-03],
         [ 1.2118e-03,  3.1261e-03,  3.2037e-03, -5.7482e-02, -1.5461e-01,
          -1.8058e-03, -5.7149e-03, -7.4521e-04,  2.9463e-04],
         [ 8.7049e-04, -3.2717e-04, -1.0188e-02,  1.1215e-02, -7.4697e-03,
          -1.3592e-03, -1.4984e-03, -3.1362e-04,  1.5780e-06],
         [ 1.2617e-04, -1.2257e-04, -6.9928e-04,  8.7431e-04, -2.5848e-03,
           1.2087e-03, -2.4723e-04, -1.6535e-05, -6.4372e-05],
         [-4.3932e-04, -1.8130e-04,  7.4368e-04, -6.1396e-04, -4.1518e-04,
           4.8132e-04,  1.6036e-04,  5.3081e-05,  1.6636e-05]],

        [[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,
          -1.1264e-02, -1.8349e-03,  7.2464e-03,  2.3807e-03],
         [ 3.2969e-03,  1.9590e-02,  2.8355e-02, -1.0493e-02, -1.3216e-01,
           1.7804e-02,  7.9768e-03,  2.1362e-03, -6.9118e-04],
         [-5.2572e-04, -4.1409e-03, -3.6560e-02,  2.1644e-02,  1.6418e-02,
           9.3557e-03,  3.3846e-03,  7.4172e-05,  1.8406e-04],
         [-1.4907e-03,  2.0496e-03, -4.8581e-03,  3.5471e-03, -2.9191e-03,
          -1.5056e-03,  7.7168e-04, -2.3136e-04, -1.2064e-05],
         [-2.3742e-04,  4.5083e-04, -1.2933e-03, -4.4028e-04,  6.4168e-04,
          -8.2755e-04,  4.1233e-04, -1.1037e-04, -6.3762e-06]]]),
 'metrics.aspect_ratio': tensor([9.6474, 9.1036, 9.4119, 9.5872]),
 'metrics.aspect_ratio_over_edge_rotational_transform': tensor([  9.3211, 106.7966,  13.8752,   8.9834]),
 'metrics.average_triangularity': tensor([-0.6456, -0.5325, -0.6086, -0.6531]),
 'metrics.axis_magnetic_mirror_ratio': tensor([0.2823, 0.4224, 0.2821, 0.2213]),
 'metrics.axis_rotational_transform_over_n_field_periods': tensor([0.2333, 0.0818, 0.1887, 0.1509]),
 'metrics.edge_magnetic_mirror_ratio': tensor([0.4869, 0.5507, 0.3029, 0.2991]),
 'metrics.edge_rotational_transform_over_n_field_periods': tensor([0.3450, 0.0284, 0.2261, 0.3557]),
 'metrics.flux_compression_in_regions_of_bad_curvature': tensor([1.4084, 0.9789, 1.5391, 1.1138]),
 'metrics.max_elongation': tensor([6.7565, 6.9036, 5.6105, 5.8703]),
 'metrics.minimum_normalized_magnetic_gradient_scale_length': tensor([5.9777, 4.2971, 8.5928, 4.8531]),
 'metrics.qi': tensor([0.0148, 0.0157, 0.0016, 0.0248]),
 'metrics.vacuum_well': tensor([-0.2297, -0.1146, -0.0983, -0.1738])}

Advanced Usage

For advanced manipulation and visualization of data contained in this dataset, install constellaration from here: pip install constellaration

Load and instantiate plasma boundaries:

from constellaration.geometry import surface_rz_fourier

ds = datasets.load_dataset(
    "proxima-fusion/constellaration",
    columns=["plasma_config_id", "boundary.json"],
    split="train",
    num_proc=4,
)
pandas_ds = ds.to_pandas().set_index("plasma_config_id")

plasma_config_id = "DQ4abEQAQjFPGp9nPQN9Vjf"
boundary_json = pandas_ds.loc[plasma_config_id]["boundary.json"]
boundary = surface_rz_fourier.SurfaceRZFourier.model_validate_json(boundary_json)

Plot boundary:

from constellaration.utils import visualization

visualization.plot_surface(boundary).show()
visualization.plot_boundary(boundary).get_figure().show()
Boundary Cross-sections
Plot of plasma boundary Plot of boundary cross-sections

Stream and instantiate the VMEC ideal MHD equilibria:

from constellaration.mhd import vmec_utils

wout_ds = datasets.load_dataset(
    "proxima-fusion/constellaration",
    "vmecpp_wout",
    split="train",
    streaming=True,
)

row = next(wout_ds.__iter__())

vmecpp_wout_json = row["json"]
vmecpp_wout = vmec_utils.VmecppWOut.model_validate_json(vmecpp_wout_json)

# Fetch corresponding boundary

plasma_config_id = row["plasma_config_id"]
boundary_json = pandas_ds.loc[plasma_config_id]["boundary.json"]
boundary = surface_rz_fourier.SurfaceRZFourier.model_validate_json(boundary_json)

Plot flux surfaces:

from constellaration.utils import visualization

visualization.plot_flux_surfaces(vmecpp_wout, boundary)

Plot of flux surfaces Save ideal MHD equilibrium to VMEC2000 WOut file:

import pathlib
from constellaration.utils import file_exporter

file_exporter.to_vmec2000_wout_file(vmecpp_wout, pathlib.Path("vmec2000_wout.nc"))

Dataset Creation

Curation Rationale

Wide-spread community progress is currently bottlenecked by the lack of standardized optimization problems with strong baselines and datasets that enable data-driven approaches, particularly for quasi-isodynamic (QI) stellarator configurations.

Source Data

Data Collection and Processing

We generated this dataset by sampling diverse QI fields and optimizing stellarator plasma boundaries to target key properties, using four different methods.

Who are the source data producers?

Proxima Fusion's stellarator optimization team.

Personal and Sensitive Information

The dataset contains no personally identifiable information.

Citation

BibTeX:

@article{article_id, author = {Santiago A Cadena, Andrea Merlo, Emanuel Laude, Alexander Bauer, Atul Agrawal, Maria Pascu, Marija Savtchouk, Lukas Bonauer, Enrico Guiraud, Stuart R. Hudson, Markus Kaiser}, title = {ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks}, journal = {NeurIPS 2025 Datasets and Benchmarks Track}, year = {2025} }

Glossary

Abbreviation Expansion
QI Quasi-Isodynamic(ity)
MHD Magneto-Hydrodynamic
DESC Dynamical Equilibrium Solver for Confinement
VMEC/VMEC++ Variational Moments Equilibrium Code (Fortran/C++)
QP Quasi-Poloidal
NAE Near-Axis Expansion
NFP Number of Field Periods

Dataset Card Authors

Alexander Bauer, Santiago A. Cadena

Dataset Card Contact

[email protected]

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