A Novel Fourier Feature Network for Solving Partial Differential Equations
Building on the foundation of single-hidden-layer neural networks, Fourier Feature Networks (FENs) are proposed, which incorporate Fourier features using $\cos$, $\sin$, or a combination of both. Similar to Extreme Learning Machines (ELMs), FENs employ a single-hidden-layer architecture to generate a set of basis functions. The target function is then approximated as a linear combination of these basis functions, with the coefficients determined using the least squares method. However, unlike ELMs, which often rely on affine transformations to improve representational power, FENs can achieve high-precision solutions without requiring such transformations on the input variables. To evaluat...
| Authors | Qihong Yang, Zhijie Su, Yangtao Deng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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