Fourier Feature Pyramids for Physics-Informed Neural Networks
Beignet improves physics-informed neural networks by replacing random Fourier feature embeddings with a trainable multi-resolution Fourier feature pyramid. This allows the model to efficiently scale accuracy by controlling parameter count rather than architecture size. It enables faster spatial derivative computation and more stable optimization for difficult PDEs, achieving higher accuracy with fewer parameters.