CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition
CAFE+FNO improves Fourier kernel generation by introducing explicit, learnable feature composition into the process. It combines Fourier and Chebyshev features and uses a multiplicative composition stage to allow the network to selectively adjust frequency interactions. This method achieves lower mean errors on four out of five PDE benchmarks compared to existing methods.