2 citations · 5 across the 13 of their papers we have counts for
4 papers · 1 filter
A Novel Fourier Feature Network for Solving Partial Differential Equations
Qihong Yang, Zhijie Su, Yangtao Deng +1
Building on the foundation of single-hidden-layer neural networks, Fourier Feature Networks (FENs) are proposed, which incorporate Fourier features using , , or a combi…
A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
Qihong Yang, Yangtao Deng, Qiaolin He +1
This paper presents the Tensor Product Network (TPNet), a novel neural architecture for efficient and accurate function approximation and PDE solving. The core of the proposal invo…
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
Qihong Yang, Qiaolin He
Solving high-frequency partial differential equations (PDEs) with neural networks is notoriously difficult due to the spectral bias of conventional architectures. We propose the Mu…
Multilevel Neural Corrections with Adaptive Sampling for Partial Differential Equations
Yu Yang, Qiaolin He
We propose a multi-level framework for deep neural PDE solvers that integrates adaptive sampling with additive neural corrections. At each level, an adaptive sampling distribution…