AI 中文总结
提出各向异性麦克斯韦神经算子（AMNO），实现EAST装置ICRH全波响应的亚秒级参数化建模，降低对密集参考场的依赖且精度优于稀疏监督傅里叶神经算子。
AI 中文摘要
不同等离子体介电条件下的离子回旋共振加热（ICRH）全波计算需要反复组装和求解大规模离散系统，这限制了参数扫描和多案例响应分析。为此，我们提出一种各向异性麦克斯韦神经算子（AMNO），用于实验先进超导托卡马克（EAST）的ICRH全波响应快速参数化建模；该模型在其他设置固定、氢 minority 份额X_H在0.01-0.05范围内变化产生的单参数介电场族中，学习从空间变化的复各向异性介电张量场到频域麦克斯韦约束下三分量复电场的共享解算子。它通过谱算子层表示全局空间耦合与局部精细尺度响应，并结合稀疏参考场监督与频域麦克斯韦方程残差。与同一EAST频域麦克斯韦-介电模型的COMSOL参考解对比显示，AMNO可重构主要空间与谱特征，且对未见插值测试案例保持稳定精度；在参考场点减少至密集全波集的7.5%的情况下，AMNO在相同监督下与稀疏监督傅里叶神经算子（FNO-Sparse）相比，相对L₂误差降低66.1%-89.9%，单案例推理仅需约0.25秒。因此，AMNO降低了对密集参考场监督的依赖，同时实现亚秒级参数化复场推理，为建模的EAST构型内的X_H范围内快速扫描与跨案例响应分析提供了物理约束且数据高效的代理模型。
英文摘要
Full-wave calculations of ion cyclotron resonance heating (ICRH) under different plasma dielectric conditions require repeated assembly and solution of large-scale discretised systems, limiting parameter sweeps and multi-case response analysis. We therefore propose an anisotropic Maxwell neural operator (AMNO) for rapid parametric modelling of ICRH full-wave responses for the Experimental Advanced Superconducting Tokamak (EAST), which learns, within the one-parameter dielectric-field family generated by varying the hydrogen minority fraction X_H over 0.01-0.05 under otherwise fixed settings, a shared solution operator from the spatially varying complex anisotropic dielectric-tensor field to the three-component complex electric field under frequency-domain Maxwell constraints. It represents global spatial coupling through spectral operator layers and local fine-scale responses, and combines sparse reference-field supervision with the frequency-domain Maxwell-equation residual. Comparisons with COMSOL reference solutions for the same EAST frequency-domain Maxwell-dielectric model show that AMNO reconstructs the principal spatial and spectral features and maintains stable accuracy for unseen interpolation test cases. With reference-field points reduced to 7.5% of the dense full-wave set, AMNO reduces the relative L_2 error by 66.1%-89.9% compared with a sparsely supervised Fourier neural operator (FNO-Sparse) under the same supervision and requires about 0.25 s for single-case inference. AMNO thus reduces dependence on dense reference-field supervision while enabling subsecond parametric complex-field inference, providing a physics-constrained and data-efficient surrogate for rapid in-range X_H sweeps and cross-case response analysis within the modelled EAST configuration.
