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Non-equispaced Fourier Neural Solvers for PDEs
v1v2 (latest)

Non-equispaced Fourier Neural Solvers for PDEs

9 December 2022
Haitao Lin
Lirong Wu
Yongjie Xu
Yufei Huang
Siyuan Li
Guojiang Zhao
Z. Stan
ArXiv (abs)PDFHTML

Papers citing "Non-equispaced Fourier Neural Solvers for PDEs"

39 / 39 papers shown
Title
Generating synthetic data for neural operators
Generating synthetic data for neural operators
Erisa Hasani
Rachel A. Ward
AI4CE
128
8
0
04 Jan 2024
STONet: A Neural-Operator-Driven Spatio-temporal Network
STONet: A Neural-Operator-Driven Spatio-temporal Network
Haitao Lin
Guojiang Zhao
Lirong Wu
Stan Z. Li
AI4TSAI4CE
61
1
0
18 Apr 2022
How Do Vision Transformers Work?
How Do Vision Transformers Work?
Namuk Park
Songkuk Kim
ViT
83
481
0
14 Feb 2022
Message Passing Neural PDE Solvers
Message Passing Neural PDE Solvers
Johannes Brandstetter
Daniel E. Worrall
Max Welling
AI4CE
88
287
0
07 Feb 2022
Adaptive Fourier Neural Operators: Efficient Token Mixers for
  Transformers
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas
Morteza Mardani
Zong-Yi Li
Andrew Tao
Anima Anandkumar
Bryan Catanzaro
84
243
0
24 Nov 2021
Neural Operator: Learning Maps Between Function Spaces
Neural Operator: Learning Maps Between Function Spaces
Nikola B. Kovachki
Zong-Yi Li
Burigede Liu
Kamyar Azizzadenesheli
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
117
453
0
19 Aug 2021
Global Filter Networks for Image Classification
Global Filter Networks for Image Classification
Yongming Rao
Wenliang Zhao
Zheng Zhu
Jiwen Lu
Jie Zhou
ViT
66
470
0
01 Jul 2021
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel
  Machines
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines
Matthew A. Wright
Joseph E. Gonzalez
67
23
0
02 Jun 2021
Intriguing Properties of Vision Transformers
Intriguing Properties of Vision Transformers
Muzammal Naseer
Kanchana Ranasinghe
Salman Khan
Munawar Hayat
Fahad Shahbaz Khan
Ming-Hsuan Yang
ViT
324
651
0
21 May 2021
Towards Robust Vision Transformer
Towards Robust Vision Transformer
Xiaofeng Mao
Gege Qi
YueFeng Chen
Xiaodan Li
Ranjie Duan
Shaokai Ye
Yuan He
Hui Xue
ViT
64
194
0
17 May 2021
Are Convolutional Neural Networks or Transformers more like human
  vision?
Are Convolutional Neural Networks or Transformers more like human vision?
Shikhar Tuli
Ishita Dasgupta
Erin Grant
Thomas Griffiths
ViTFaML
56
185
0
15 May 2021
MLP-Mixer: An all-MLP Architecture for Vision
MLP-Mixer: An all-MLP Architecture for Vision
Ilya O. Tolstikhin
N. Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
...
Andreas Steiner
Daniel Keysers
Jakob Uszkoreit
Mario Lucic
Alexey Dosovitskiy
423
2,682
0
04 May 2021
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Xiangxiang Chu
Zhi Tian
Yuqing Wang
Bo Zhang
Haibing Ren
Xiaolin K. Wei
Huaxia Xia
Chunhua Shen
ViT
82
1,026
0
28 Apr 2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Ze Liu
Yutong Lin
Yue Cao
Han Hu
Yixuan Wei
Zheng Zhang
Stephen Lin
B. Guo
ViT
455
21,439
0
25 Mar 2021
Attention is Not All You Need: Pure Attention Loses Rank Doubly
  Exponentially with Depth
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong
Jean-Baptiste Cordonnier
Andreas Loukas
132
386
0
05 Mar 2021
Conditional Local Convolution for Spatio-temporal Meteorological
  Forecasting
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
Haitao Lin
Zhangyang Gao
Yongjie Xu
Lirong Wu
Ling Li
Stan. Z. Li
AI4TS
189
77
0
04 Jan 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
664
41,369
0
22 Oct 2020
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
500
2,444
0
18 Oct 2020
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Lei Bai
Lina Yao
Can Li
Xianzhi Wang
Can Wang
GNNAI4TS
86
1,188
0
06 Jul 2020
Multipole Graph Neural Operator for Parametric Partial Differential
  Equations
Multipole Graph Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
216
392
0
16 Jun 2020
Learning continuous-time PDEs from sparse data with graph neural
  networks
Learning continuous-time PDEs from sparse data with graph neural networks
V. Iakovlev
Markus Heinonen
Harri Lähdesmäki
AI4CE
81
70
0
16 Jun 2020
The Random Feature Model for Input-Output Maps between Banach Spaces
The Random Feature Model for Input-Output Maps between Banach Spaces
Nicholas H. Nelsen
Andrew M. Stuart
76
144
0
20 May 2020
Model Reduction and Neural Networks for Parametric PDEs
Model Reduction and Neural Networks for Parametric PDEs
K. Bhattacharya
Bamdad Hosseini
Nikola B. Kovachki
Andrew M. Stuart
217
332
0
07 May 2020
EikoNet: Solving the Eikonal equation with Deep Neural Networks
EikoNet: Solving the Eikonal equation with Deep Neural Networks
Jonathan D. Smith
Kamyar Azizzadenesheli
Zachary E. Ross
42
132
0
25 Mar 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations
Neural Operator: Graph Kernel Network for Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
199
748
0
07 Mar 2020
DeepONet: Learning nonlinear operators for identifying differential
  equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
248
2,150
0
08 Oct 2019
Latent ODEs for Irregularly-Sampled Time Series
Latent ODEs for Irregularly-Sampled Time Series
Yulia Rubanova
Ricky T. Q. Chen
David Duvenaud
BDLAI4TS
84
259
0
08 Jul 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of
  Non-linear Dynamics With Guaranteed Stability
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
Shaowu Pan
Karthik Duraisamy
68
138
0
09 Jun 2019
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural
  networks
ODE2^22VAE: Deep generative second order ODEs with Bayesian neural networks
Çağatay Yıldız
Markus Heinonen
Harri Lähdesmäki
BDLDRL
68
88
0
27 May 2019
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse
  Problems
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems
Leah Bar
N. Sochen
28
71
0
10 Apr 2019
Convolutional Self-Attention Networks
Convolutional Self-Attention Networks
Baosong Yang
Longyue Wang
Derek F. Wong
Lidia S. Chao
Zhaopeng Tu
53
126
0
05 Apr 2019
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
417
5,111
0
19 Jun 2018
The Deep Ritz method: A deep learning-based numerical algorithm for
  solving variational problems
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
E. Weinan
Ting Yu
121
1,387
0
30 Sep 2017
DGM: A deep learning algorithm for solving partial differential
  equations
DGM: A deep learning algorithm for solving partial differential equations
Justin A. Sirignano
K. Spiliopoulos
AI4CE
91
2,063
0
24 Aug 2017
Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer
S. Schoenholz
Patrick F. Riley
Oriol Vinyals
George E. Dahl
593
7,485
0
04 Apr 2017
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Youngjoo Seo
M. Defferrard
P. Vandergheynst
Xavier Bresson
GNN
148
775
0
22 Dec 2016
Layer Normalization
Layer Normalization
Jimmy Lei Ba
J. Kiros
Geoffrey E. Hinton
413
10,494
0
21 Jul 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral
  Filtering
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
M. Defferrard
Xavier Bresson
P. Vandergheynst
GNN
353
7,669
0
30 Jun 2016
Diffusion-Convolutional Neural Networks
Diffusion-Convolutional Neural Networks
James Atwood
Don Towsley
GNNDiffM
197
1,256
0
06 Nov 2015
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