Physics-Informed Visual MARFE Prediction on the HL-3 Tokamak
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908617163145216 |
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| author | Dong, Qianyun Li, Rongpeng Yang, Zongyu Xia, Fan Liu, Liang Zhao, Zhifeng Zhong, Wulyu |
| author_facet | Dong, Qianyun Li, Rongpeng Yang, Zongyu Xia, Fan Liu, Liang Zhao, Zhifeng Zhong, Wulyu |
| contents | The Multifaceted Asymmetric Radiation From the Edge (MARFE) is a critical plasma instability that often precedes density-limit disruptions in tokamaks, posing a significant risk to machine integrity and operational efficiency. Early and reliable alert of MARFE formation is therefore essential for developing effective disruption mitigation strategies, particularly for next-generation devices like ITER. This paper presents a novel, physics-informed indicator for early MARFE prediction and disruption warning developed for the HL-3 tokamak. Our framework integrates two core innovations: (1) a high-fidelity label refinement pipeline that employs a physics-scored, weighted Expectation-Maximization (EM) algorithm to systematically correct noise and artifacts in raw visual data from cameras, and (2) a continuous-time, physics-constrained Neural Ordinary Differential Equation (Neural ODE) model that predicts the short-horizon ``worsening" of a MARFE. By conditioning the model's dynamics on key plasma parameters such as normalized density ($f_G$, derived from core electron density) and core electron temperature ($T_e$), the predictor achieves superior performance in the low-false-alarm regime crucial for control. On a large experimental dataset from HL-3, our model demonstrates high predictive accuracy, achieving an Area Under the Curve (AUC) of 0.969 for 40ms-ahead prediction. The indicator has been successfully deployed for real-time operation with updates every 1 ms. This work lays a very foundation for future proactive MARFE mitigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Visual MARFE Prediction on the HL-3 Tokamak Dong, Qianyun Li, Rongpeng Yang, Zongyu Xia, Fan Liu, Liang Zhao, Zhifeng Zhong, Wulyu Plasma Physics The Multifaceted Asymmetric Radiation From the Edge (MARFE) is a critical plasma instability that often precedes density-limit disruptions in tokamaks, posing a significant risk to machine integrity and operational efficiency. Early and reliable alert of MARFE formation is therefore essential for developing effective disruption mitigation strategies, particularly for next-generation devices like ITER. This paper presents a novel, physics-informed indicator for early MARFE prediction and disruption warning developed for the HL-3 tokamak. Our framework integrates two core innovations: (1) a high-fidelity label refinement pipeline that employs a physics-scored, weighted Expectation-Maximization (EM) algorithm to systematically correct noise and artifacts in raw visual data from cameras, and (2) a continuous-time, physics-constrained Neural Ordinary Differential Equation (Neural ODE) model that predicts the short-horizon ``worsening" of a MARFE. By conditioning the model's dynamics on key plasma parameters such as normalized density ($f_G$, derived from core electron density) and core electron temperature ($T_e$), the predictor achieves superior performance in the low-false-alarm regime crucial for control. On a large experimental dataset from HL-3, our model demonstrates high predictive accuracy, achieving an Area Under the Curve (AUC) of 0.969 for 40ms-ahead prediction. The indicator has been successfully deployed for real-time operation with updates every 1 ms. This work lays a very foundation for future proactive MARFE mitigation. |
| title | Physics-Informed Visual MARFE Prediction on the HL-3 Tokamak |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2510.24347 |