Disruption prediction for future tokamaks using parameter-based transfer learning

Zheng, Wei; Xue, Fengming; Chen, Zhongyong; Chen, Dalong; Guo, Bihao; Shen, Chengshuo; Ai, Xinkun; Wang, Nengchao; Zhang, Ming; Ding, Yonghua; Chen, Zhipeng; Zhang, Yang; Shen, Biao; Xiao, Bingjia; Pan, Yuan

doi:10.1038/s42005-023-01296-9

Cited by 17 publications

(10 citation statements)

References 49 publications

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“…The dataset and its split are similar to our previous works [24,52]. A split of datasets is shown in table 2.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…J-TEXT is a medium-sized tokamak with a major radius R = 1.05 m and a minor radius a = 0.25 m [51]. The typical discharges on the J-TEXT are characterized by a plasma current (I P ) of approximately 200 kA, a toroidal field (B t ) of around 2.0 T, a pulse length of 700-800 ms, plasma densities (n e ) ranging from 1 to 7 × 10 19 m −3 , and an electron temperature (T e ) of about 1 keV as the limiter configuration [52]. The typical resistive time scales in J-TETX is about 25 ms (τ R ≈ 25 ms).…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Cross-tokamak disruption prediction based on domain adaptation

Shen,

Zheng,

Guo

et al. 2024

Nucl. Fusion

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The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges. The approach aims to predict disruption by finding a feature space that is universal to all tokamak. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying domain adaptation in the task of cross-tokamak disruption prediction. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak. This approach provides a light, interpretable and few data-required way by aligning features to predict disruption using small data volume from the future tokamak.

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“…The dataset and its split are similar to our previous works [24,52]. A split of datasets is shown in table 2.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Cross-tokamak disruption prediction based on domain adaptation

Shen,

Zheng,

Guo

et al. 2024

Nucl. Fusion

Self Cite

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“…With the popularity of machine learning, there are more and more successful cases based on machine learning in the field of fusion, which provides new ideas for the study of fusion. The most famous cases include plasma disruption prediction based on deep learning [7] and plasma profile control based on reinforcement learning [8]. Machine learning can mainly be divided into two categories: traditional machine learning and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Plasma current tomography for HL-2A based on Bayesian inference

LIU 刘,

WANG 王,

WU 吴

et al. 2024

Plasma Sci. Technol.

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An accurate plasma current profile has irreplaceable value for the steady state operation of the plasma. In this study, the plasma current tomography based on Bayesian inference is applied to HL-2A device and used to reconstruct the plasma current profile, two different Bayesian probability priors are tried, namely the Conditional AutoRegressive (CAR) prior and the Advanced Squared Exponential (ASE) kernel prior. Compared to the CAR prior, the ASE kernel prior adopts non-stationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters, which can make the shape of the current profilemore flexible in space. The results indicate that the ASE prior couples more information, reduces the probability of unreasonable solutions, and achieves higher reconstruction accuracy.

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“…There are two distinguished ways to predict disruptions in fusion research: physics-based approach [7][8][9] and datadriven approach [5,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The former has good interpretability and physical consistency for predicting disruptions through physical models combined with MHD theory.…”

Section: Introductionmentioning

confidence: 99%

Enhancing disruption prediction through Bayesian neural network in KSTAR

Kim,

Lee,

Seo

et al. 2024

Plasma Phys. Control. Fusion

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In this research, we develop a data-driven disruption predictor based on Bayesian deep probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR. Unlike conventional neural networks within a frequentist approach, Bayesian neural networks can quantify the uncertainty associated with their predictions, thereby enhancing the precision of disruption prediction by mitigating false alarm rates through uncertainty thresholding. Leveraging 0D plasma parameters from EFIT and diagnostic data, a Temporal Convolutional Network adept at handling multi-time scale data was utilized. The proposed framework demonstrates proficiency in predicting disruptions, substantiating its effectiveness through successful applications to KSTAR experimental data.

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Disruption prediction for future tokamaks using parameter-based transfer learning

Cited by 17 publications

References 49 publications

Cross-tokamak disruption prediction based on domain adaptation

Cross-tokamak disruption prediction based on domain adaptation

Plasma current tomography for HL-2A based on Bayesian inference

Enhancing disruption prediction through Bayesian neural network in KSTAR

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