东华大学团队用PNOT模型在EAST上搞定偏滤器温度场实时重建，比传统FEM快得多，还自带物理约束，做聚变仿真的可以看看。
传统有限元法(FEM)计算偏滤器温度场耗时且不适合实时应用。研究者提出物理感知神经算子Transformer(PNOT)，将边界热流关系建模为结构化图，利用图注意力捕捉空间物理依赖。PNOT通过物理感知神经算子模块聚合相似物理条件的查询点并模拟热扩散，结合梯度约束Sobolev正则化损失保证函数值与导数一致性。实验表明，该方法在EAST装置上实现了高精度温度场重建，物理约束显著提升预测保真度。
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
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