MS-SFNN builds PDE solutions from element-wise products of outputs from d independent fixed-random-weight subnetworks with tunable scaling and cosine activations, then solves coefficients by least squares, claiming superior accuracy on high-frequency problems.
Spectral connvergece of random feature method in one dimension.arXiv preprint arXiv:2507.07371, 2025
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Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
MS-SFNN builds PDE solutions from element-wise products of outputs from d independent fixed-random-weight subnetworks with tunable scaling and cosine activations, then solves coefficients by least squares, claiming superior accuracy on high-frequency problems.