CNN disruption predictor at JET: Early versus late data fusion approach

Aymerich, E.; Sias, Giuliana; Pisano, F.; Cannas, B.; Fanni, Alessandra; the-JET-Contributors,

doi:10.1016/j.fusengdes.2023.113668

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Disruption prediction models, including this work, aim to be used for disruption avoidance [61][62][63] or mitigation [64]. This work focused on developing a reliable event alarm to predict the occurrence of disruptions with the Bayesian framework.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Data-driven disruption prediction, benefiting from decades of data accumulation during the operation of tokamaks, is a highly feasible approach for disruption prediction. Numerous data-driven disruption predictors have been developed on JET [7][8][9][10][11][12], ASDEX-U [13], DIII-D [14,15], C-Mod [14,16], JT-60U [17], HL-2A [18,19], EAST [20][21][22], and J-TEXT [23][24][25] with high accuracy on their own tokamaks. However, the high-performance operation of future tokamaks imposes a significant cost for unmitigated disruption, making it impractical to achieve large data for training such models.…”

Section: Introductionmentioning

confidence: 99%

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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