SII-LWY/constellaration-data

Name: SII-LWY/constellaration-data
Creator: SII-LWY
Published: 2026-03-14 13:20:28
License: 暂无描述

Hugging Face2026-03-14 更新2026-03-29 收录

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--- dataset_info: - config_name: default features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: boundary.json dtype: string - name: boundary.is_stellarator_symmetric dtype: bool - name: boundary.n_field_periods dtype: int64 - name: boundary.n_periodicity dtype: int64 - name: boundary.r_cos sequence: sequence: float64 - name: boundary.r_sin dtype: 'null' - name: boundary.z_cos dtype: 'null' - name: boundary.z_sin sequence: sequence: float64 - name: omnigenous_field_and_targets.id dtype: string - name: omnigenous_field_and_targets.json dtype: string - name: omnigenous_field_and_targets.aspect_ratio dtype: float64 - name: omnigenous_field_and_targets.major_radius dtype: float64 - name: omnigenous_field_and_targets.max_elongation dtype: float64 - name: omnigenous_field_and_targets.omnigenous_field.modB_spline_knot_coefficients sequence: sequence: float64 - name: omnigenous_field_and_targets.omnigenous_field.n_field_periods dtype: int64 - name: omnigenous_field_and_targets.omnigenous_field.poloidal_winding dtype: int64 - name: omnigenous_field_and_targets.omnigenous_field.torodial_winding dtype: int64 - name: omnigenous_field_and_targets.omnigenous_field.x_lmn sequence: sequence: sequence: float64 - name: omnigenous_field_and_targets.rotational_transform dtype: float64 - name: desc_omnigenous_field_optimization_settings.id dtype: string - name: desc_omnigenous_field_optimization_settings.json dtype: string - name: desc_omnigenous_field_optimization_settings.equilibrium_settings.check_orientation dtype: bool - name: desc_omnigenous_field_optimization_settings.equilibrium_settings.max_poloidal_mode dtype: int64 - name: desc_omnigenous_field_optimization_settings.equilibrium_settings.max_toroidal_mode dtype: int64 - name: desc_omnigenous_field_optimization_settings.equilibrium_settings.psi dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.aspect_ratio dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.elongation dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.is_iota_positive dtype: bool - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.is_stellarator_symmetric dtype: bool - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.major_radius dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.max_elongation dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.max_poloidal_mode dtype: int64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.max_toroidal_mode dtype: int64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.mirror_ratio dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.n_field_periods dtype: int64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.rotational_transform dtype: float64 - name: desc_omnigenous_field_optimization_settings.initial_guess_settings.torsion dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.name dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.target_kind dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.aspect_ratio_settings.weight dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.name dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.target_kind dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.elongation_settings.weight dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_M_factor dtype: int64 - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_N_factor dtype: int64 - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eq_lcfs_grid_rho dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.eta_weight dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.name dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.omnigenity_settings.weight dtype: float64 - name: desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.name dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.target_kind dtype: string - name: desc_omnigenous_field_optimization_settings.objective_settings.rotational_transform_settings.weight dtype: float64 - name: desc_omnigenous_field_optimization_settings.optimizer_settings.maxiter dtype: int64 - name: desc_omnigenous_field_optimization_settings.optimizer_settings.name dtype: string - name: desc_omnigenous_field_optimization_settings.optimizer_settings.verbose dtype: int64 - name: vmec_omnigenous_field_optimization_settings.id dtype: string - name: vmec_omnigenous_field_optimization_settings.json dtype: string - name: vmec_omnigenous_field_optimization_settings.gradient_based_relative_objectives_tolerance dtype: float64 - name: vmec_omnigenous_field_optimization_settings.gradient_free_budget_per_design_variable dtype: int64 - name: vmec_omnigenous_field_optimization_settings.gradient_free_max_time dtype: int64 - name: vmec_omnigenous_field_optimization_settings.gradient_free_optimization_hypercube_bounds dtype: float64 - name: vmec_omnigenous_field_optimization_settings.infinity_norm_spectrum_scaling dtype: float64 - name: vmec_omnigenous_field_optimization_settings.max_poloidal_mode dtype: int64 - name: vmec_omnigenous_field_optimization_settings.max_toroidal_mode dtype: int64 - name: vmec_omnigenous_field_optimization_settings.n_inner_optimizations dtype: int64 - name: vmec_omnigenous_field_optimization_settings.use_continuation_method_in_fourier_space dtype: bool - name: vmec_omnigenous_field_optimization_settings.verbose dtype: bool - name: qp_init_omnigenous_field_optimization_settings.id dtype: string - name: qp_init_omnigenous_field_optimization_settings.json dtype: string - name: qp_init_omnigenous_field_optimization_settings.aspect_ratio dtype: float64 - name: qp_init_omnigenous_field_optimization_settings.elongation dtype: float64 - name: qp_init_omnigenous_field_optimization_settings.is_iota_positive dtype: bool - name: qp_init_omnigenous_field_optimization_settings.is_stellarator_symmetric dtype: bool - name: qp_init_omnigenous_field_optimization_settings.major_radius dtype: float64 - name: qp_init_omnigenous_field_optimization_settings.mirror_ratio dtype: float64 - name: qp_init_omnigenous_field_optimization_settings.n_field_periods dtype: int64 - name: qp_init_omnigenous_field_optimization_settings.torsion dtype: float64 - name: nae_init_omnigenous_field_optimization_settings.id dtype: string - name: nae_init_omnigenous_field_optimization_settings.json dtype: string - name: nae_init_omnigenous_field_optimization_settings.aspect_ratio dtype: float64 - name: nae_init_omnigenous_field_optimization_settings.max_elongation dtype: float64 - name: nae_init_omnigenous_field_optimization_settings.max_poloidal_mode dtype: int64 - name: nae_init_omnigenous_field_optimization_settings.max_toroidal_mode dtype: int64 - name: nae_init_omnigenous_field_optimization_settings.mirror_ratio dtype: float64 - name: nae_init_omnigenous_field_optimization_settings.n_field_periods dtype: int64 - name: nae_init_omnigenous_field_optimization_settings.rotational_transform dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.has_optimize_boundary_omnigenity_vmec_error dtype: bool - name: misc.has_optimize_boundary_omnigenity_desc_error dtype: bool - name: misc.has_generate_qp_initialization_from_targets_error dtype: bool - name: misc.has_generate_nae_initialization_from_targets_error dtype: bool - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 1240651097.3174775 num_examples: 182222 download_size: 610201645 dataset_size: 1240651097.3174775 - config_name: finite_beta_1pct features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.source_plasma_config_id dtype: string - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 76508664.04284965 num_examples: 112804 download_size: 52556502 dataset_size: 76508664.04284965 - config_name: finite_beta_2pct features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.source_plasma_config_id dtype: string - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 47402708.67692375 num_examples: 82722 download_size: 38593722 dataset_size: 47402708.67692375 - config_name: finite_beta_3pct features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.source_plasma_config_id dtype: string - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 25395079.54877086 num_examples: 55448 download_size: 25947395 dataset_size: 25395079.54877086 - config_name: finite_beta_4pct features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.source_plasma_config_id dtype: string - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 13509704.764567751 num_examples: 36444 download_size: 17086365 dataset_size: 13509704.764567751 - config_name: finite_beta_5pct features: - name: metrics.id dtype: string - name: metrics.json dtype: string - name: metrics.aspect_ratio dtype: float64 - name: metrics.aspect_ratio_over_edge_rotational_transform dtype: float64 - name: metrics.average_triangularity dtype: float64 - name: metrics.axis_magnetic_mirror_ratio dtype: float64 - name: metrics.axis_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.edge_magnetic_mirror_ratio dtype: float64 - name: metrics.edge_rotational_transform_over_n_field_periods dtype: float64 - name: metrics.flux_compression_in_regions_of_bad_curvature dtype: float64 - name: metrics.max_elongation dtype: float64 - name: metrics.minimum_normalized_magnetic_gradient_scale_length dtype: float64 - name: metrics.qi dtype: float64 - name: metrics.vacuum_well dtype: float64 - name: misc.vmecpp_wout_id dtype: string - name: misc.source_plasma_config_id dtype: string - name: misc.has_neurips_2025_forward_model_error dtype: bool - name: plasma_config_id dtype: string splits: - name: train num_bytes: 8792377.219300479 num_examples: 26729 download_size: 12538180 dataset_size: 8792377.219300479 - config_name: vmecpp_wout features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 - config_name: vmecpp_wout_finite_beta_1pct features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 - config_name: vmecpp_wout_finite_beta_2pct features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 - config_name: vmecpp_wout_finite_beta_3pct features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 - config_name: vmecpp_wout_finite_beta_4pct features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 - config_name: vmecpp_wout_finite_beta_5pct features: - name: plasma_config_id dtype: string - name: id dtype: string - name: json dtype: string splits: - name: train num_bytes: 1100757693175 num_examples: 148292 download_size: 956063943 dataset_size: 1100757693175 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: finite_beta_1pct data_files: - split: train path: finite_beta_1pct/train-* - config_name: finite_beta_2pct data_files: - split: train path: finite_beta_2pct/train-* - config_name: finite_beta_3pct data_files: - split: train path: finite_beta_3pct/train-* - config_name: finite_beta_4pct data_files: - split: train path: finite_beta_4pct/train-* - config_name: finite_beta_5pct data_files: - split: train path: finite_beta_5pct/train-* - config_name: vmecpp_wout data_files: - split: train path: vmecpp_wout/part* - config_name: vmecpp_wout_finite_beta_1pct data_files: - split: train path: vmecpp_wout_finite_beta_1pct/part* - config_name: vmecpp_wout_finite_beta_2pct data_files: - split: train path: vmecpp_wout_finite_beta_2pct/part* - config_name: vmecpp_wout_finite_beta_3pct data_files: - split: train path: vmecpp_wout_finite_beta_3pct/part* - config_name: vmecpp_wout_finite_beta_4pct data_files: - split: train path: vmecpp_wout_finite_beta_4pct/part* - config_name: vmecpp_wout_finite_beta_5pct data_files: - split: train path: vmecpp_wout_finite_beta_5pct/part* license: mit language: - en tags: - physics - fusion - optimization - neurips pretty_name: ConStellaration size_categories: - 100K<n<1M --- # 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. The performance metrics and ideal MHD equilibria were evaluated under vacuum (default) and with plasma inside (finite beta). ## 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](assets/mhd_intro_v2.png) ### Dataset Sources  - **Repository:** https://huggingface.co/datasets/proxima-fusion/constellaration - **Paper:** https://arxiv.org/abs/2506.19583 - **Code:** https://github.com/proximafusion/constellaration ## Dataset Structure  There are 6 tuples of datasets, one for each percentage of volume-averaged plasma inside the boundary: <table> <tr> <th>Condition</th> <th>Boundaries, Metrics, Generation Settings, Misc</th> <th>Ideal MHD Equilibira</th> </tr> <tr> <th>Vacuum</th> <th>default</th> <th>vmecpp_wout</th> </tr> <tr> <th>1% Beta</th> <th>finte_beta_1pct</th> <th>vmecpp_wout_finite_beta_1pct</th> </tr> <tr> <th>2% Beta</th> <th>finte_beta_2pct</th> <th>vmecpp_wout_finite_beta_2pct</th> </tr> <tr> <th>3% Beta</th> <th>finte_beta_3pct</th> <th>vmecpp_wout_finite_beta_3pct</th> </tr> <tr> <th>4% Beta</th> <th>finte_beta_4pct</th> <th>vmecpp_wout_finite_beta_4pct</th> </tr> <tr> <th>5% Beta</th> <th>finte_beta_5pct</th> <th>vmecpp_wout_finite_beta_5pct</th> </tr> </table> <br> Contents of datasets: <table> <tr> <th style="border-right: 1px solid gray;">default</th> <th>vmecpp_wout</th> </tr> <tr> <td style="border-right: 1px solid gray;"> Contains information about: <ul> <li>Plasma boundaries</li> <li>Ideal MHD metrics in vacuum</li> <li>Omnigenous field and targets, used as input for sampling of plasma boundaries</li> <li>Sampling settings for various methods (DESC, VMEC, QP initialization, Near-axis expansion)</li> <li>Miscellaneous information <ul> <li>the corresponding ideal MHD equilibrium ID in <b>vmecpp_wout</b></li> <li>errors that might have occurred during sampling or metrics computation.</li> </ul> </li> </ul> </td> <td> Contains: <ul> <li>For each plasma boundary in <b>default</b>, a JSON-string representation of the "WOut" file as obtained when running VMEC, initialized on the boundary.<br>The JSON representation can be converted to a VMEC2000 output file.</li> <li>The corresponding plasma configuration ID in <b>default</b></li> </ul> </td> </tr> <tr> <td colspan="2"> The <b>default</b> (vacuum) subset above is special in the sense that it contains more information than the other subsets (finite betas) below. Those are derived from the <b>default</b> (vacuum) subset by setting for each plasma boundary the respective volume-averaged beta percentage and re-computing the performance metrics and ideal MHD equilibria: </td> </tr> <tr> <th style="border-right: 1px solid gray;">finite_beta_*pct</th> <th>vmecpp_wout_finite_beta_*pct</th> </tr> <tr> <td style="border-right: 1px solid gray;"> Contains information about: <ul> <li>Ideal MHD metrics with plasma</li> <li>Miscellaneous information <ul> <li>the corresponding source plasma configuration ID in <b>default</b></li> <li>the corresponding ideal MHD equilibrium ID in <b>vmecpp_wout_finite_beta_*pct</b></li> <li>errors that might have occurred metrics computation.</li> </ul> </li> </ul> </td> <td> Same as <b>vmecpp_wout</b> above, corresponding to <b>finite_beta_*pct</b> </td> </tr> </table> 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). ## 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`): ```python 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 ``` <div style="margin-left: 1em;"> <details> <summary>Output</summary> ```python {'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])} ``` </details> </div> ### Advanced Usage For advanced manipulation and visualization of data contained in this dataset, install `constellaration` from [here](https://github.com/proximafusion/constellaration): `pip install constellaration` Load and instantiate plasma boundaries: ```python 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: ```python from constellaration.utils import visualization visualization.plot_surface(boundary).show() visualization.plot_boundary(boundary).get_figure().show() ``` Boundary | Cross-sections :-------------------------:|:-------------------------: ![Plot of plasma boundary](assets/boundary.png) | ![Plot of boundary cross-sections](assets/boundary_cross_sections.png) Stream and instantiate the VMEC ideal MHD equilibria: ```python 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: ```python from constellaration.utils import visualization visualization.plot_flux_surfaces(vmecpp_wout, boundary) ``` ![Plot of flux surfaces](assets/flux_surfaces.png) Save ideal MHD equilibrium to *VMEC2000 WOut* file: ```python import pathlib from constellaration.utils import file_exporter file_exporter.to_vmec2000_wout_file(vmecpp_wout, pathlib.Path("vmec2000_wout.nc")) ``` Match the boundaries from the **default** dataset to the corresponding metrics under a certain plasma condition: ```python import datasets # Load default dataset to get the boundaries default_ds = datasets.load_dataset( "proxima-fusion/constellaration", split="train", num_proc=4, ) # Load finite beta 3% dataset finite_beta_3pct_ds = datasets.load_dataset( "proxima-fusion/constellaration", name="finite_beta_3pct", split="train", num_proc=4, ) # Join the two datasets on plasma_config_id <-> misc.source_plasma_config_id default_df = ( default_ds .to_pandas() .set_index("plasma_config_id") .filter(like="boundary.") ) finite_beta_3pct_df = ( finite_beta_3pct_ds .to_pandas() .set_index("misc.source_plasma_config_id") ) finite_beta_3pct_with_boundaries_df = ( finite_beta_3pct_df .join(default_df, how="inner") # joins on index .reset_index(names="misc.source_plasma_config_id") ) ``` ## 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{cadena2025constellaration, title={ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks}, author={Cadena, Santiago A and Merlo, Andrea and Laude, Emanuel and Bauer, Alexander and Agrawal, Atul and Pascu, Maria and Savtchouk, Marija and Guiraud, Enrico and Bonauer, Lukas and Hudson, Stuart and others}, journal={arXiv preprint arXiv:2506.19583}, year={2025} } ``` ## Glossary  | Abbreviation | Expansion | | -------- | ------- | | QI | Quasi-Isodynamic(ity) | | MHD | Magneto-Hydrodynamic | | [DESC](https://desc-docs.readthedocs.io/en/stable/) | Dynamical Equilibrium Solver for Confinement | | VMEC/[VMEC++](https://github.com/proximafusion/vmecpp) | 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 alexbauer@proximafusion.com

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