Fourier Feature Pyramids for Physics-Informed Neural Networks

In three linesBeignet, a new neural network architecture for solving partial differential equations (PDEs), replaces random Fourier feature embeddings in PINNs with a trainable multi-resolution Fourier feature pyramid. The model efficiently computes spatial derivatives via FFT and achieves higher accuracy with fewer parameters than existing PINN methods.

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