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A Phase Shift Deep Neural Network for High Frequency Approximation and
  Wave Problems

A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems

23 September 2019
Wei Cai
Xiaoguang Li
Lizuo Liu
ArXivPDFHTML

Papers citing "A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems"

14 / 14 papers shown
Title
Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
Siavash Khodakarami
Vivek Oommen
Aniruddha Bora
George Karniadakis
AI4CE
67
2
0
17 Mar 2025
Approximation of Solution Operators for High-dimensional PDEs
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
30
0
0
18 Jan 2024
Deep Neural-network Prior for Orbit Recovery from Method of Moments
Deep Neural-network Prior for Orbit Recovery from Method of Moments
Y. Khoo
Sounak Paul
N. Sharon
24
3
0
28 Apr 2023
Phase-shifted Adversarial Training
Phase-shifted Adversarial Training
Yeachan Kim
Seongyeon Kim
Ihyeok Seo
Bonggun Shin
AAML
OOD
24
0
0
12 Jan 2023
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High
  Level Accuracy and Efficiency
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency
Weilong Guan
Kai-Ping Yang
Yinsheng Chen
Zhong Guan
PINN
AI4CE
18
12
0
19 Oct 2022
On the Activation Function Dependence of the Spectral Bias of Neural
  Networks
On the Activation Function Dependence of the Spectral Bias of Neural Networks
Q. Hong
Jonathan W. Siegel
Qinyan Tan
Jinchao Xu
34
23
0
09 Aug 2022
Overview frequency principle/spectral bias in deep learning
Overview frequency principle/spectral bias in deep learning
Z. Xu
Yaoyu Zhang
Tao Luo
FaML
33
66
0
19 Jan 2022
Subspace Decomposition based DNN algorithm for elliptic type multi-scale
  PDEs
Subspace Decomposition based DNN algorithm for elliptic type multi-scale PDEs
Xi-An Li
Z. Xu
Lei Zhang
24
27
0
10 Dec 2021
Gradient-enhanced physics-informed neural networks for forward and
  inverse PDE problems
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Jeremy Yu
Lu Lu
Xuhui Meng
George Karniadakis
PINN
AI4CE
38
451
0
01 Nov 2021
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for
  Solving PDEs
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs
Lulu Zhang
Tao Luo
Yaoyu Zhang
Weinan E
Z. Xu
Zheng Ma
AI4CE
27
33
0
08 Jul 2021
Reproducing Activation Function for Deep Learning
Reproducing Activation Function for Deep Learning
Senwei Liang
Liyao Lyu
Chunmei Wang
Haizhao Yang
36
21
0
13 Jan 2021
Friedrichs Learning: Weak Solutions of Partial Differential Equations
  via Deep Learning
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
Fan Chen
J. Huang
Chunmei Wang
Haizhao Yang
28
30
0
15 Dec 2020
Fourier-domain Variational Formulation and Its Well-posedness for
  Supervised Learning
Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning
Tao Luo
Zheng Ma
Zhiwei Wang
Zhi-Qin John Xu
Yaoyu Zhang
OOD
47
4
0
06 Dec 2020
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs
Wei Cai
Zhi-Qin John Xu
AI4CE
22
38
0
25 Oct 2019
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