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arXiv · 2606.19853

Physics-Informed Neural Network with Squeeze-Excitation-like Attention

Yun-Fei Song·Long-Gang Pang·Fu-Peng Li·Jun-Jie Zhang
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Abstract

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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Yun-Fei Song, Long-Gang Pang, Fu-Peng Li, Jun-Jie Zhang. 2026-06-18. Physics-Informed Neural Network with Squeeze-Excitation-like Attention. https://arxiv.org/abs/2606.19853

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