2 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
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…
Solving Multi-Group Neutron Diffusion Eigenvalue Problem with Decoupling Residual Loss Function
Shupei Yu, Qiaolin He, Shiquan Zhang +3
In the midst of the neural network's success in solving partial differential equations, tackling eigenvalue problems using neural networks remains a challenging task. However, the…
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
Yu Yang, Qihong Yang, Yangtao Deng +1
In this work, we propose an end-to-end adaptive sampling neural network (MMPDE-Net) based on the moving mesh method, which can adaptively generate new sampling points by solving th…
On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem
Yu Yang, Helin Gong, Qihong Yang +3
In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) \cite…
Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems
Qihong Yang, Yangtao Deng, Yu Yang +2
In this article, we propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems. These neural networks share similar…
A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems
Yu Yang, Helin Gong, Shiquan Zhang +4
We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In…