将注意力机制引入物理信息神经网络，提升高频率问题求解稳定性与精度。
Physics-Informed Neural Network with Squeeze-Excitation-like Attention
- 在PINN中加入类Squeeze-Excitation注意力，动态调整各层神经元重要性。
- 17/20基准测试中初始损失显著降低，方差近乎为零，优化起点更稳定。
- 无需傅里叶特征或周期激活函数，即可媲美专用模型，适合各类物理建模场景。
我们提出SEA-PINN，一种将类Squeeze-Excitation注意力机制融入物理信息神经网络的新架构，以动态重校准跨层神经元的重要性。SEA-PINN的关键优势在于高度稳定的初始化：在20个基准问题中的17个上，其初始损失显著降低且方差几乎可忽略，为优化提供了准确定的起始点。值得注意的是，在未使用傅里叶特征嵌入或周期性激活函数的情况下，SEA-PINN在高频问题7上的准确率达83%，相较FNN-PINN提升90%；而将其集成至专为高频设计的TSA-PINN后，性能进一步提升42.49%。这些结果表明，SEA-PINN是一个轻量级可插拔模块，能增强非线性表征能力，促进更鲁棒高效的收敛，并提升物理信息学习的整体可靠性。
原文摘要 · Abstract (English)
We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the importance of neurons across layers. A key feature of SEA-PINN is its highly stable initialization. On 17 out of 20 benchmark problems, SEA-PINN exhibit nearly negligible variance and significantly reduced initial loss, establishing a quasi-deterministic and favorable starting point for optimization. Notably, without employing Fourier feature embeddings or periodic activation functions, SEA-PINN attained competitive accuracy (83\% vs. 90\% improvement relative to FNN-PINN on the high-frequency case 7) as compared with TSA-PINN-a model specifically engineered for high-frequency problems via learnable frequencies in sinusoidal activations. Furthermore, integrating SEA-PINN into TSA-PINN boosted performance by 42.49\%. These results underscore SEA-PINN as a lightweight plug-in module that enhances nonlinear representation power, promotes more robust and efficient convergence, and strengthens the overall reliability of physics-informed learning.
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