Physics Informed Random Feature Neural Networks for Solving PDEs
Machine learning-based partial differential equations (PDEs) solvers have attracted significant attention in recent years. Most progress in this area has been driven by deep neural networks such as physics-informed neural networks (PINNs) and kernel method (such as physics-informed Gaussian Processes). We introduce a physics-informed random feature method for countering part of the spectral bias which PINN-based solvers are facing for a certain class of PDEs. Random feature method was originally proposed to approximate large-scale kernel machines and can be viewed as a specialized randomized neural network. Compared to other state-of-the-art PINN-based solvers which require a large number...
| Authors | Chi-An Chen, Chunyang Liao, Ming Zhong |
|---|---|
| Primary category | math.NA |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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