Problem
Research questions and friction points this paper is trying to address.
disruption prediction
finite horizon
tokamak
Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms
🤖 AI Summary
研究通过引入早期事件预测方法，如TLS和survTLS，改进托卡马克中断预警系统，以提高在有限预测时间内的中断风险预测准确性。
📝 Abstract
Reliable disruption prediction is essential for the safe operation of future tokamaks. Existing full-distribution survival methods model the complete residual time-to-disruption distribution, whereas operational decisions primarily depend on disruption risk within a finite prediction horizon. This mismatch motivates introducing Early Event Prediction (EEP) objectives into survival-based disruption prediction. We take Deep Survival Machines (DSM) as the full-distribution baseline and propose applying two established EEP methods to tokamak disruption prediction: Temporal Label Smoothing (TLS), which directly predicts disruption probability within a finite horizon, and survTLS, which additionally models the event-time distribution within that horizon. Using a common causal encoder, we compare these methods on DIII-D, Alcator C-Mod, and EAST. We distinguish threshold-free deadline ranking from validation-selected fixed-policy alarm performance and evaluate prediction horizons and encoder architectures. TLS achieves the best mean alarm performance on DIII-D and EAST, whereas all methods perform poorly on Alcator C-Mod. survTLS does not consistently outperform DSM, suggesting that directly learning horizon-level event probability is more effective than modeling detailed within-horizon event-time distributions in the present setting. Finally, the selected prediction horizons and encoder-ablation results vary across devices, reflecting differences in disruption characteristics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Early Event Prediction
Temporal Label Smoothing
survTLS
Deep Survival Machines
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