发表机构
Anhui University; Institute of Plasma Physics, Chinese Academy of Sciences（安徽大学; 中国科学院等离子体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对核聚变托卡马克诊断中多模态多任务学习框架缺失的问题，提出FusionMMT统一框架，集成视觉与时间序列数据，在EAST-VTD640数据集上实现破裂预测、ELM识别和H98回归，性能优于现有方法。
AI 中文摘要
随着全球能源需求的日益增长，核聚变已成为未来清洁能源的一个有前景的方向。托卡马克是磁约束聚变的主要途径之一。要实现高性能、长脉冲和稳态运行，需要对等离子体状态进行有效诊断。然而，现有的智能诊断方法大多局限于多模态单任务或单模态多任务学习，而统一的多模态多任务学习框架仍未得到充分探索。为填补这一空白，我们构建了EAST-VTD640，这是一个多模态多任务数据集，整合了来自640次EAST放电的视觉和时间序列诊断数据，用于破裂预测、边缘局域模（ELM）识别和H98回归。在此基础上，我们提出了FusionMMT，这是首个用于智能托卡马克等离子体诊断的统一多模态多任务框架。FusionMMT采用多尺度、时间感知和变量感知建模来处理异构采样率和高频序列的高计算成本。它进一步将任务自适应多模态融合与渐进式多任务优化相结合，以学习共享和任务特定的表示，同时缓解跨任务冲突和优化不平衡。在EAST-VTD640上的大量实验表明，FusionMMT在破裂预测、ELM识别和H98回归方面均优于代表性的多模态多任务方法。源代码将在该https URL上发布。
英文摘要
With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the leading approaches to magnetic-confinement fusion. Achieving high-performance, long-pulse, and steady-state operation requires effective diagnosis of plasma states. However, existing intelligent diagnostic methods are largely limited to either multimodal single-task or unimodal multitask learning, while a unified multimodal multitask learning framework remains underexplored. To address this gap, we construct EAST-VTD640, a multimodal multitask dataset that integrates vision and time-series diagnostics from 640 EAST shots for disruption prediction, edge-localized mode (ELM) recognition, and H98 regression. On this basis, we present FusionMMT, the first unified multimodal multitask framework for intelligent tokamak plasma diagnostics. FusionMMT employs multi-scale, time-aware, and variable-aware modeling to handle heterogeneous sampling rates and the high computational cost of high-frequency sequences. It further combines task-adaptive multimodal fusion with progressive multitask optimization to learn shared and task-specific representations while mitigating cross-task conflicts and optimization imbalance. Extensive experiments on EAST-VTD640 show that FusionMMT outperforms representative multimodal multitask methods across disruption prediction, ELM recognition, and H98 regression. The source code will be released on https://github.com/Event-AHU/OpenFusion
