用神经网络直接从二维边界推断三维太阳磁场，精度和物理一致性双提升。
Physics-informed Attention-enhanced Fourier Neural Operator for Solar Magnetic Field Extrapolations
- 融合注意力与空洞卷积，强化对磁场关键特征的捕捉能力。
- 在ISEE数据集上，预测精度超越现有最先进模型，且符合力无平衡与散度为零的物理约束。
- 适合从事太阳物理建模、磁场反演及深度学习应用的研究者。
我们提出物理信息注意力增强型傅里叶神经算子（PIANO），用于解决太阳物理学中的非线性无体力场（NLFFF）问题。与依赖迭代数值方法的传统方法不同，PIANO可直接从二维边界条件学习三维磁场结构。具体而言，该模型结合高效通道注意力（ECA）与空洞卷积（DC），通过优先关注与磁场变化相关的关键通道，提升对多模态输入的表征能力。此外，在训练过程中引入物理信息损失，强制满足无体力和散度为零的物理条件，确保预测结果具有高物理一致性。在ISEE NLFFF数据集上的实验表明，PIANO不仅在精度上优于当前最先进的神经算子，且在多个太阳活动区重构的磁场中均表现出良好的物理特性一致性。项目代码已开源：https://github.com/Autumnstar-cjh/PIANO。
原文摘要 · Abstract (English)
We propose Physics-informed Attention-enhanced Fourier Neural Operator (PIANO) to solve the Nonlinear Force-Free Field (NLFFF) problem in solar physics. Unlike conventional approaches that rely on iterative numerical methods, our proposed PIANO directly learns the 3D magnetic field structure from 2D boundary conditions. Specifically, PIANO integrates Efficient Channel Attention (ECA) mechanisms with Dilated Convolutions (DC), which enhances the model's ability to capture multimodal input by prioritizing critical channels relevant to the magnetic field's variations. Furthermore, we apply physics-informed loss by enforcing the force-free and divergence-free conditions in the training process so that our prediction is consistent with underlying physics with high accuracy. Experimental results on the ISEE NLFFF dataset show that our PIANO not only outperforms state-of-the-art neural operators in terms of accuracy but also shows strong consistency with the physical characteristics of NLFFF data across magnetic fields reconstructed from various solar active regions. The GitHub of this project is available https://github.com/Autumnstar-cjh/PIANO
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