SEA-PINN adds squeeze-excitation attention to PINNs, yielding stable low-variance initialization and competitive accuracy on 17 of 20 benchmarks without Fourier features or periodic activations.
While both models learn the difficult features inside the training domain, the FNN-PINN produces highly unstable and incorrect predictions outside of it
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Physics-Informed Neural Network with Squeeze-Excitation-like Attention
SEA-PINN adds squeeze-excitation attention to PINNs, yielding stable low-variance initialization and competitive accuracy on 17 of 20 benchmarks without Fourier features or periodic activations.