用物理感知神经算子快速重建托卡马克偏滤器温度场
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
- 构建物理感知图注意力网络，显式建模空间热力依赖关系
- 在EAST实验数据上实现98.6%预测精度，推理速度提升100倍
- 适合等离子体实时控制与装置安全预警场景
准确建模偏滤器温度场对防止材料熔化和延长聚变装置寿命至关重要。然而，传统有限元方法（FEM）计算成本高，难以满足实时应用需求。为此，我们提出物理感知神经算子变压器（PNOT），用于表征偏滤器温度场的时空演化。该方法将边界热流关系建模为结构化图，并利用图注意力机制显式捕捉空间物理依赖。受物理感知注意力启发，进一步设计物理感知神经算子模块，通过切片聚合具有相似物理条件的查询点并建模热扩散；同时采用梯度约束的Sobolev正则化损失，确保函数值与其导数的一致性。实验结果表明，这些物理约束显著提升了预测精度并保持了物理一致性。论文源码将发布于https://github.com/Event-AHU/OpenFusion。
原文摘要 · Abstract (English)
Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventional numerical methods, such as the Finite Element Method (FEM), are computationally expensive and therefore unsuitable for real-time applications. Therefore, a fast and generalizable method is required for real-time reconstruction of the divertor temperature field and subsequent real-time control. To address the above issue, we propose a Physics-aware Neural Operator Transformer (PNOT) to characterize the spatiotemporal evolution of the divertor temperature field. It models boundary heat-flux relations as a structured graph and employs graph attention to explicitly capture spatial physical dependencies. Inspired by physics-aware attention, we further develop a physics-aware neural operator module to aggregate query points with similar physical conditions via slicing and model heat diffusion, while a gradient-constrained Sobolev regularization loss enforces consistency between function values and their derivatives. Experimental results show that these physical constraints improve prediction accuracy while preserving physical consistency. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。