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configuration_id string | schema_version string | boundary.n_field_periods int64 | boundary.r_cos list | boundary.z_sin list | metrics.aspect_ratio float64 | metrics.edge_rotational_transform_over_n_field_periods float64 | metrics.average_triangularity float64 | metrics.max_elongation float64 | metrics.edge_magnetic_mirror_ratio float64 | metrics.vacuum_well float64 | metrics.minimum_normalized_magnetic_gradient_scale_length float64 | metrics.qi float64 | metrics.flux_compression_in_regions_of_bad_curvature float64 | evaluation.success bool | evaluation.error string | evaluation.fidelity string | evaluation.solver_version string | evaluation.feasibility float64 | source.name string | source.path string | source.record string | quality.iota_representation string | quality.metric_count int32 | quality.flags list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | 9.420256 | 0.250088 | 0.442329 | 5.360968 | 0.258242 | 0.010774 | 4.804288 | 0.000314 | 0.908133 | true | null | high | 0.2.6 | 0.032967 | optimization_history | r226_18_nfp2_a7p99-10p10_cma00 | 5 | signed | 9 | [] |
End of preview.
SII Stellarator Configuration Dataset
Wenyang Li | PhD student, Shanghai Innovation Institute
AI-Driven Controlled Fusion Simulation, Control & Design Lab
Email: lwydsg@mail.nankai.edu.cn | Phone: +86-156-2003-5216. License: CC BY 4.0.
2026-10-21: SOTA Configuration Update
This release includes the configurations from our first-place ConStellaration MHD-stable QI submission and related CMA optimization evaluations.
On the official leaderboard, the submission by Wenyang_Li achieved a hypervolume score of 248.227515058408, ranking 1st in the saved 2026-10-19 leaderboard snapshot. Submitted on 2026-10-18, it scored approximately 1.25% above the runner-up's 245.1589221336.
All 783 submitted configurations have been verified by exact configuration_id lookup in the prepared release across main/ and cons_bench/.
Official leaderboard comparison from the 2026-10-19 snapshot.
CMA search results for NFP 2, 3 and 4, from the article's 2026-10-20 analysis snapshot. Stars mark reevaluated starting configurations; colored points show the evolved Pareto fronts in aspect ratio and L_delta_b.
Overview
Stellarator boundary configurations and recorded physics evaluations, covering NFP 1-5. This replaces the earlier 158,685-row release. The original data came from proxima-fusion/constellaration. New sources include sBO/CMA, traditional AL, earlier BO, DP, ExLLM and FusionLeague evaluations. Incremental maintenance also collects reviewed benchmark physics evaluations.
Collection updated (UTC, per-file snapshots): 2026-11-02; schema: stellarator-1.1.
| Directory | Configurations | Parquet shards |
|---|---|---|
main/ |
4,600,170 | 93 |
failed/ |
207,573 | 5 |
1,862,985 main rows have all nine metrics.
The main table retains successful evaluations. Failed records are stored separately.
Fields and Quality
Flat column names are retained. Missing values are null.
| Fields | Type and meaning |
|---|---|
configuration_id, schema_version |
Strings: canonical configuration SHA-256 and format version |
boundary.n_field_periods |
int64: NFP |
boundary.r_cos, boundary.z_sin |
Nested float64 lists: stellarator-symmetric Fourier boundary |
metrics.* |
Nine nullable float64 diagnostics listed below |
evaluation.success |
Nullable bool; null means unknown, not failure |
evaluation.error, .fidelity, .solver_version |
Nullable strings: original error and recorded evaluation settings |
evaluation.feasibility |
Nullable float64: source score, not a universal constraint definition |
source.name, .path, .record |
Strings: source family, relative file path, record locator |
quality.iota_representation |
signed, absolute_only or unknown |
quality.metric_count, .flags |
Count of finite metrics and list of quality/transformation flags |
The .field abbreviations in a row retain that row's prefix, e.g. source.path.
Metric suffix after metrics. |
Meaning |
|---|---|
aspect_ratio |
Aspect ratio |
edge_rotational_transform_over_n_field_periods |
Edge iota divided by NFP |
average_triangularity |
Average triangularity |
max_elongation |
Maximum elongation |
edge_magnetic_mirror_ratio |
Edge magnetic mirror ratio |
vacuum_well |
Vacuum magnetic well |
minimum_normalized_magnetic_gradient_scale_length |
L_delta_b |
qi |
QI diagnostic, not log10(QI) |
flux_compression_in_regions_of_bad_curvature |
Bad-curvature flux compression |
- Deduplication: exact NFP plus canonical Fourier coefficients. Prefer successful, then unknown, then failed evaluations; within a status prefer more complete metrics, then deterministic source order.
- Boundary: usually 5x9; higher-order matrices such as 8x15 are also retained. For
(M, 2N+1), rows arem=0..M-1, columnsn=-N..N. Smaller supported grids are zero-padded; higher modes are preserved. Coefficients are not normalized or approximately rounded for deduplication. - Use limits: metric completeness does not establish physical feasibility or comparable solver fidelity.
Read the Data
import pyarrow.dataset as ds
data = ds.dataset("main", format="parquet")
complete = data.to_table(filter=(ds.field("quality.metric_count") == 9)
& (ds.field("evaluation.success") == True))
For signed-iota training also require quality.iota_representation == "signed";
for raw-QI-only studies exclude qi_reconstructed_from_log10 in quality.flags.
Maintain the Dataset
FusionLeague/datasets/stellarator/ is the editing source: main/, failed/, these two READMEs and tools/.
Edit here first; the external HF clone is only a publication copy. Tools and audit archives are not uploaded.
Step 1: update the source dataset on Linux.
Start from the FusionLeague/ repository root:
cd datasets/stellarator && python tools/update_dataset.py
If dependencies are missing, run python -m pip install -r tools/requirements.txt once, then retry.
Wait for a successful update before copying; do not update data while copying. Add --dry-run to inspect sources only.
- Sources: automatically scan
methods/for realqi/qi_frontierevaluation journals; skip quadratic/examples and duplicate run exports. Collectbenchmarks/qi_design/datasubmissions only when review, result hash, submission identity and point inputs agree. Running or unapproved results are deferred. No solvers or surrogate inference run. - Incremental merge: index the current dataset on the first run, then remember completed log positions. Read new complete records, defer unfinished final lines, and apply the same exact deduplication/whole-record preference above. Unchanged sources are skipped; changed log prefixes are re-imported without deleting historical configurations. Revoked reviews do not automatically retract previously collected data.
- Output: without winning additions/replacements, leave Parquet and READMEs unchanged. Otherwise export and fully verify a sorted dataset, replace
main/andfailed/, and refresh only the statistics blocks in both READMEs. Ingestion is incremental, but changed datasets are fully re-exported and verified. - State and recovery:
tools/.update-state/holds the SQLite index, cursors, lock, latest summary, reports and previous published dataset. Only one updater may run. After an error/interruption, keep this state and rerun the same command to recover; do not manually remove its lock or edit/replace indexed Parquet files. Do not read/copy/push during installation or after an unfinished update. - Resources and audit: first indexing takes longer; allow space for the index, staged output and previous dataset in addition to current data. The latest
last_update.jsonpoints to the report/verification/quarantine; invalid records are also kept in the indexissuestable. Review notices and quality/provenance before publication. Earlier publication backups are cleaned after a successful update; this is not a full version archive.
The original full-source builder remains in tools/build_dataset.py for separate rebuilds; routine
updates do not rescan all historical optimization directories. Original snapshot audits remain at
../_archive/stellarator-20260927/releases/20260923-full-v1/. Local fixture tests:
python -m unittest discover -s tools -p 'test_*.py'.
Publish to Hugging Face
Run in this order: download from the official Hub into the external clone → merge maintenance additions → review and publish. Commands use Bash and assume FusionLeague/ and the external clone are side by side.
One-time setup (skip if already cloned). Install Git LFS and run from the FusionLeague/ root; these commands leave you in that directory:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/SII-AI4Fusion/SII-Stellarator-Configuration-Dataset ../SII-Stellarator-Configuration-Dataset &&
git -C ../SII-Stellarator-Configuration-Dataset lfs install --local
Step 1: download complete data from the official Hub. Start from the FusionLeague/ root. If you just ran an update from the maintenance directory, use cd ../.. to return to the root. The external clone should be on main with no uncommitted changes:
cd ../SII-Stellarator-Configuration-Dataset &&
GIT_LFS_SKIP_SMUDGE=1 git pull --ff-only https://huggingface.co/datasets/SII-AI4Fusion/SII-Stellarator-Configuration-Dataset main &&
git -c lfs.url=https://huggingface.co/datasets/SII-AI4Fusion/SII-Stellarator-Configuration-Dataset.git/info/lfs lfs pull --include="" --exclude="" &&
git lfs fsck HEAD
This updates the Git revision, downloads all LFS files for that revision, then verifies their integrity. Continue to Step 2 after Git LFS fsck OK; you are now in the external clone. For download-only use, stop here. After an interrupted download, remain in the external clone and repeat the git -c ... lfs pull ... and git lfs fsck HEAD commands above; synchronize only after they succeed.
To use the mirror, replace the domain in both URLs with hf-mirror.com; the mirror may lag behind.
Step 2: synchronize local additions into the external clone. After preparing local maintenance data and completing Step 1, run the next commands without changing directories:
python -m pip install -r ../FusionLeague/datasets/stellarator/tools/requirements.txt &&
python ../FusionLeague/datasets/stellarator/tools/prepare_publish.py --hf-repo . &&
cp -r ../FusionLeague/datasets/stellarator/assets . &&
git add README.md README.zh.md assets &&
git diff --cached --stat
Install dependencies (including duckdb) into the current Python environment before running synchronization; the merge tool does not download from the Hub again. It uses the data downloaded in Step 1 as the baseline and merges maintenance data by configuration_id. Duplicate configurations keep a whole evaluation, preferring success, metric completeness and source order. cons_bench also merges submission memberships. Existing other subsets, extra columns, descriptions and configurations are retained. Output is installed and staged in the external clone; local maintenance data and its incremental index stay unchanged. The old delete-and-copy commands are no longer used.
The terminal shows merge, shard and verification progress. Incomplete downloads or uncommitted edits stop preparation. Temporary files and recovery state are kept in the external clone's .git/stellarator-publish/. Do not edit either dataset directory during merging.
Step 3: review and publish. Review the changes, then remain in the external clone and run; skip if nothing changed:
git commit -m "Update consolidated stellarator dataset" &&
git lfs fsck HEAD &&
git push origin main
Commands use Bash; stop on any error. Commit, verify LFS, then push; tools, caches and archives are not uploaded. If push is rejected, do not force-push: retain local commits, resolve Git divergence, then synchronize and merge again. Keep recovery state after an interrupted merge and rerun Step 2.
Update Notes
This maintenance update (2026-10-17):
- Adds samples produced by an earlier combined optimization algorithm and merges them with existing data using the established deduplication rules.
- Adds the separate
cons_benchsubset, collecting public configurations and published evaluations submitted by ConStellaration leaderboard users while retaining submission provenance. Thank you to the participants for sharing their results; source and licensing details are provided below.
The dataset will receive incremental updates from time to time. Follow SII-AI4Fusion on Hugging Face and like this dataset to stay connected with future updates.
ConStellaration Leaderboard Solutions
cons_bench is a separate subset of public results for all three tasks on the ConStellaration leaderboard. It is stored in cons_bench/ and is not merged into main/. Run from this dataset directory:
python -m pip install -r tools/requirements.txt
python tools/update_cons_bench.py
The source is the leaderboard's public results repository. Repeated runs download only new or changed files. A successful run updates the separate Parquet and bilingual statistics and registers the HF cons_bench subset with a train split. Keep tools/.cons-bench-state/ after a network interruption and rerun the same command. To use the mirror, run HF_ENDPOINT=https://hf-mirror.com python tools/update_cons_bench.py.
Each exact unique configuration has one row. benchmark.entries retains all submission links, public usernames, times, tasks, scores, per-task snapshot ranks and point objectives/feasibility, preserving repeated submission provenance. MHD scores belong to whole submitted sets, not individual points. benchmark.boundary_json retains the representative's original boundary; benchmark.problem_types lists its tasks. Row-level metrics come from the first entry in benchmark.entries, preferring the evaluation with more available metrics.
Only published metrics are retained: maximum elongation for geometrical, L_delta_b for simple-to-build, and aspect plus L_delta_b for MHD. Unpublished metrics, solver status and versions remain null; VMEC is not rerun. source.revision and pinned source links identify the upstream snapshot. source.license retains the upstream license ID or null when unspecified; this repository's CC BY 4.0 declaration does not grant rights on behalf of the upstream source. Shared configuration_id values can overlap the main table, so this subset is not automatically a disjoint test set. Submissions by user test are excluded, matching the leaderboard page.
Publication merge: 2026-11-02; 193 submissions, 16,966 unique configurations.
from datasets import load_dataset
bench = load_dataset("SII-AI4Fusion/SII-Stellarator-Configuration-Dataset",
"cons_bench", split="train")
2026-10-17 CMA / NGO Incremental Update
- CMA: this round added 37,632 configurations to the maintenance source and replaced 1 existing record.
- NGO: this round added 101,190 configurations to the maintenance source.
In total, 138,822 configurations were added and 22 duplicate records were ignored; the export passed verification. These are additions relative to the local maintenance source, not net additions to HF. Publication retains existing HF data and recomputes the final total after merging and deduplication.
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