标题： 基于变压器的数据驱动模型在ADITYA托卡马克装置中当前淬火事件的早期预测 显示英文标题

标题： Early Prediction of Current Quench Events in the ADITYA Tokamak using Transformer based Data Driven Models

摘要： 托卡马克等离子体中的扰动，表现为突然的热和电流淬灭，对面向等离子体的部件和系统完整性构成严重威胁。 在扰动发生前足够早的预测对于实施有效的缓解策略至关重要。 本研究提出了一种新的数据驱动方法，利用基于变压器的深度学习模型对ADITYA托卡马克诊断数据进行早期电流淬灭预测，这是扰动的关键前兆。 使用多变量时间序列数据，变压器模型在各种数据分布和预测阈值下均优于LSTM基线。 变压器模型具有更好的召回率，在预测阈值达到8-10毫秒时仍能保持高于0.9的数值，显著优于LSTM在此关键指标上的表现。 所提出的方案在8毫秒的提前时间内保持稳健，为ADITYA托卡马克的扰动缓解提供了实际可行性。 此外，全面的数据多样性分析和偏差敏感性研究强调了模型的泛化能力。 这项工作首次将变压器架构应用于ADITYA托卡马克数据以进行早期电流淬灭预测，为短脉冲托卡马克的实时扰动避免奠定了有前景的基础。 显示英文摘要

摘要： Disruptions in tokamak plasmas, marked by sudden thermal and current quenches, pose serious threats to plasma-facing components and system integrity. Accurate early prediction, with sufficient lead time before disruption onset, is vital to enable effective mitigation strategies. This study presents a novel data-driven approach for predicting early current quench, a key precursor to disruptions, using transformer-based deep learning models, applied to ADITYA tokamak diagnostic data. Using multivariate time series data, the transformer model outperforms LSTM baselines across various data distributions and prediction thresholds. The transformer model achieves better recall, maintaining values above 0.9 even up to a prediction threshold of 8-10 ms, significantly outperforming LSTM in this critical metric. The proposed approach remains robust up to an 8 ms lead time, offering practical feasibility for disruption mitigation in ADITYA tokamak. In addition, a comprehensive data diversity analysis and bias sensitivity study underscore the generalization of the model. This work marks the first application of transformer architectures to ADITYA tokamak data for early current-quench prediction, establishing a promising foundation for real time disruption avoidance in short-pulse tokamaks.