7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagova

PFNN：Less data and better performance on disruption prediction via
physics-informed deep learningZongyu Yang1, Yuhang Liu1, Wulyu Zhong1, Xiaolan Zou2, Fan Xia1, Yipo Zhang1, Xiaobo Zhu1, Yiren Zhu1,Guoliang Xiao1, Boda Yuan1, Ruihai Tong1, Linzi Liu1, Anshu Liang1, Jiao Yin1, Yu Zhou1 and Zhengwei Chen1.
1 Southwestern Institute of Physics, Chengdu, China
2 CEA, IRFM, F-13108 Saint-Paul-lez-Durance, Francee-mail (speaker): zy-yang@swip.ac.cn
Disruption prediction and mitigation is a crucial topic,
especially for future large-scale tokamaks, due to disruption’s
concomitant harmful effects on the devices. Recent progresses
have proved that deep neural network can accurately predictthe coming disruptions by learning from history experimental
data, which becomes a potential solution for the disruption
prediction in future devices [1, 2]. This technique routine has
also been proved in HL-2A tokamak, both in offline testingand online experiment [3, 4, 5]. However, a key issue about
this technique routine is whether the deep learning model canbe developed on future devices, since these devices can only
tolerate a few disruptions and therefore can’t provide muchtraining data [6]. In this research, a predict-first neuralnetwork (PFNN) is developed in HL-2A. Two predict-firsttasks are designed to embed physical knowledge into theneural network. Ablation experiments show that the embedded
physical information significantly improve the algorithms’performance, especially when the training data is limited.
The first predict-first task is to let the neural network
predict the evolution of electronic temperature (Te), electronic
density (Ne) and horizontal displacement (Dh) according to
the control target, control actuators and the plasma state. Apreparatory neural network based on encoder-decoder
framework [7] is trained on this task and three empiricalequations are hidden in the design of the neural network. Afterthe training, the neural network learns the three equations andcan accurately predict the evolution of Te, Ne and Dh, asshown in figure 1. Then this preparatory neural network canbe used as a feature extractor in the disruption prediction
algorithm and use the three equations to promote theperformance of disruption prediction.
The second predict-first task is to mask part of theexperimental data and let the neural network restore them.
Another preparatory neural network based on masked

auto-encoder framework [8] is trained on this task. It canrealistically reconstruct the masked parts according to theunmasked parts and the correlation between different input
signals. This preparatory neural network can also be used as afeature extractor in the disruption prediction algorithm anduse the correlation between different input signals to promote
the performance of disruption prediction.
Ablation experiments show that the embedded physicalinformation significantly improves the algorithms’
performance. When the amount of training shots is limited to1283 shots, the AUC (area under receiver-operator
characteristic curve) of PFNN is about 4% higher than theordinary one, as shown in figure 2. In general, PFNN, which
is pretrained by predict-first tasks to learn physicalinformation and then trained for disruption prediction in thesecond step, performs much better on disruption prediction
when the amount of training data is limited. It can be apotential solution for future tokamaks’ disruption prediction
problem.
References[1] J.K. Harbeck, A. Svyatkovskiy, et al. Nature, 2019, 568
526-531.
[2] J.X. Zhu, C. Rea, et al. Nucl. Fusion, 2021, 61(11) 114005[3] Z.Y. Yang, F. Xia et al. Nucl. Fusion. 2020, 60(1) 016017[4] Z.Y. Yang, F. Xia et al. Nucl. Fusion. 2021, 61(12) 126042[5] Z.Y. Yang, F. Xia et al. Fusion Eng. Des. 2022, 182113223[6] P. C. de Vries, Gabriella Pautasso, et al. Fusion Sci. andTech. 2016, 69(2): 471-484.
[7] Cho K, V. Merriënboer B, et al. arXiv preprint
arXiv:1406.1078, 2014.
[8] K.M. He, X.L. Chen, et al. Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition.
2022: 16000-16009.

Figure 1. a), b) and c) are electron density, electron temperatureand horizontal displacement, respectively. On the left is thecomparison of algorithm inputs and target outputs, where anobvious delay of ΔT can be observed. Here ΔT is 30ms for Neand Te, while it is 10ms for Dh. On the right is the comparisonof predicted parameters and target outputs, they are much closer
than the situation in the left subfigures.

Figure 2. Comperison between the receiver-operator
characteristic curves of baseline(red) and PFNN(blue)algorithms. Obviously PFNN performs better than thebaseline algorithm, which is trained from scratch without
the embedded physical knowledge.