| --- |
| pretty_name: CLT-IML Tokamak MHD Simulation Database |
| license: other |
| task_categories: |
| - tabular-regression |
| tags: |
| - plasma-physics |
| - tokamak |
| - magnetohydrodynamics |
| - disruption-prediction |
| - surrogate-modeling |
| - scientific-machine-learning |
| - tabular |
|
|
| --- |
| |
| # CLT-IML Tokamak MHD Simulation Database |
|
|
| > **Access and use.** This database is source-available for academic |
| > communication, inspection, and reproducibility assessment. It is not open |
| > data. Copyright (c) 2026 Zhejiang University. All rights reserved. Any use |
| > requires prior written permission from Zhejiang University or its duly |
| > authorized representative. See [Terms of access and use](#terms-of-access-and-use). |
|
|
| ## Dataset summary |
|
|
| This dataset contains tabular scalar responses, sampled two-dimensional fields, |
| test fields, and reference visualizations derived from CLT tokamak |
| magnetohydrodynamic simulations. It supports the three surrogate-modeling tasks |
| implemented in the companion CLT-IML code repository: |
|
|
| 1. regression from equilibrium/profile parameters to scalar simulation |
| outcomes; |
| 2. pressure-crash regression and thresholded disruption classification; and |
| 3. two-dimensional perturbation-field reconstruction with a neural operator. |
|
|
| The data are numerical simulation products and are not experimental plasma |
| measurements. The CLT solver and complete raw outputs from the underlying |
| simulation campaigns are not part of this dataset. |
|
|
| ## Associated manuscript |
|
|
| **Manuscript title:** *Millisecond prediction of tokamak disruptions using a |
| first-principles surrogate model* (manuscript in preparation) |
|
|
| **Authors:** W. Zhang, Y. J. Ma, S. Z. Cai, Z. W. Ma, Z. M. Sheng, and Y. |
| Zhang. W. Zhang and Y. J. Ma contributed equally. W. Zhang and Z. W. Ma are |
| the corresponding authors. |
|
|
| Affiliations: |
|
|
| 1. Institute for Fusion Theory and Simulation, School of Physics, Zhejiang |
| University, Hangzhou 310027, China. |
| 2. College of Control Science and Engineering, Zhejiang University, Hangzhou |
| 310027, China. |
|
|
| ## Repository structure |
|
|
| ```text |
| . |
| ├── Double_Tearing_Train_Database_Bisland_Ek.csv |
| ├── Transferlearning_Database_Bisland_Database_Bisland.csv |
| ├── pressure_crash_cls.csv |
| ├── TMONet-test_by_p0.csv |
| ├── Double_Tearing_Train_Database_by_p0.csv # exact Task III train manifest |
| ├── data_frame_by_p0/ # dense field tables/reference plots |
| ├── selected_B9_633/ # sampled Task III training fields |
| └── TMON-test_by_p0/ # Task III test fields/reference plots |
| ``` |
|
|
| The release includes the exact Task III training manifest used with the sampled |
| fields. Every `folder_name` in that manifest resolves to one CSV in |
| `selected_B9_633/` after `/` and `\` are replaced by `__`. |
|
|
| ## Primary tables |
|
|
| | File | Rows | Purpose | |
| | -------------------------------------------------------- | ---: | ------------------------------------ | |
| | `Double_Tearing_Train_Database_Bisland_Ek.csv` | 195 | Task I scalar-response data | |
| | `Transferlearning_Database_Bisland_Database_Bisland.csv` | 24 | Target-domain transfer-learning data | |
| | `pressure_crash_cls.csv` | 246 | Task II pressure-crash data | |
| | `TMONet-test_by_p0.csv` | 22 | Task III test-case manifest | |
|
|
| Row counts exclude CSV headers. |
|
|
| ## Data fields |
|
|
| The scalar tables use combinations of `p0`, `r1`, `r2`, `s1`, and `s2` as |
| inputs. Targets include inner/outer magnetic-island widths, linear growth rate, |
| kinetic energy, and central-pressure crash percentage. Field CSVs use spatial |
| coordinates (`X`, `Z` or `R`, `Z`) and `Value`; sampled training fields also |
| contain `Gradient`. |
|
|
| Coordinate shifts, axis swaps, normalization, logarithmic transforms, and |
| target-extraction rules are implemented in the companion code repository. See |
| its `docs/DATA_AND_MODELS.md` and extraction scripts before combining or |
| reprocessing tables. |
|
|
| ## Intended uses |
|
|
| - reproducing the surrogate-model experiments in the associated manuscript; |
| - benchmarking scalar regression and neural-operator methods on CLT-derived |
| simulation outputs; |
| - studying mappings between designed equilibrium parameters and MHD outcomes; |
| - validating or extending the supplied post-processing workflow. |
|
|
| ## Limitations |
|
|
| - Results are specific to the simulated configurations, parameter ranges, |
| normalization, and CLT setup used to generate the database. |
| - The data should not be interpreted as direct experimental measurements or as |
| a validated real-time control system. |
| - Extrapolation outside the sampled parameter domain is not guaranteed to be |
| physically meaningful. |
| - Exact numerical reproduction can depend on software versions, hardware, and |
| nondeterministic GPU operations. |
|
|
| ## Terms of access and use |
|
|
| Copyright (c) 2026 Zhejiang University. All rights reserved. |
|
|
| This database, including its tables, field data, manifests, visualizations, |
| metadata, and organization, is made publicly viewable for academic |
| communication, inspection, and reproducibility assessment. It is |
| **source-available**, but it is **not open data** and is not released under a |
| Creative Commons, Open Data Commons, or other open license. The |
| `license: other` value in the dataset-card metadata denotes these custom, |
| permission-required terms. |
|
|
| Public availability, downloading, or possession of a copy does not by itself |
| grant permission to use the database. Except to the extent expressly permitted |
| by applicable law, any use, reproduction, modification, adaptation, extraction, |
| creation or publication of derived data or results, model training or |
| evaluation, distribution, redistribution, sublicensing, deployment, |
| commercialization, or other exploitation of all or any substantial portion of |
| the database requires prior written permission from Zhejiang University or its |
| duly authorized representative. Rights not expressly granted in a written |
| permission or license agreement are reserved by Zhejiang University. |
|
|
| Permission to use the companion CLT-IML software does not automatically |
| authorize use of this database, and permission to use the database does not |
| automatically authorize use of the software. Requests for permission should be |
| directed to Zhejiang University or its duly authorized representative and |
| should identify the requester, institution, intended purpose, requested |
| materials, and planned form of use. |
|
|
| THE DATABASE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS |
| FOR A PARTICULAR PURPOSE, ACCURACY, COMPLETENESS, AND NONINFRINGEMENT. TO THE |
| MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, ZHEJIANG UNIVERSITY AND THE |
| DATABASE CREATORS SHALL NOT BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER |
| LIABILITY ARISING FROM, OUT OF, OR IN CONNECTION WITH THE DATABASE OR ITS USE. |
|
|
| ## Citation |
|
|
| Authorized users should cite the associated manuscript and identify the exact |
| version or Hugging Face revision of the database used. |
