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 only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.

翻译：未来托卡马克数据驱动破裂预测模型对破裂放电数据的高获取成本与巨大需求之间存在本质矛盾。本文提出一种基于领域自适应算法CORAL的创新方法，仅需少量未来托卡马克放电数据即可实现破裂预测。这是领域自适应技术首次应用于破裂预测任务。该方法通过将未来托卡马克（目标域）的少量数据与现有托卡马克（源域）的大量数据进行特征对齐，在现有托卡马克上训练机器学习模型。为模拟现有与未来托卡马克场景，我们选取J-TEXT作为现有托卡马克，EAST作为未来托卡马克。为模拟未来托卡马克破裂数据匮乏的情况，仅从EAST选取100例非破裂放电和10例破裂放电作为目标域训练数据。我们改进CORAL算法使其更适配破裂预测任务，提出监督式CORAL方法。与双托卡马克混合数据训练的模型相比，监督式CORAL模型可将未来托卡马克的破裂预测性能提升30%（AUC值从0.764提升至0.890）。通过可解释性分析发现，监督式CORAL可使数据分布更接近未来托卡马克特性。基于SHAP分析设计了评估模型是否学习到相似特征趋势的方法，证明监督式CORAL模型与基于大规模EAST数据训练的模型具有更高相似度。FTDP通过特征对齐方式，为未来托卡马克提供了一种轻量化、可解释且仅需少量数据的破裂预测实现方案。