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A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data

27 May 2025
Rami Cassia
Rich Kerswell
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data"

15 / 15 papers shown
Title
Flow reconstruction in time-varying geometries using graph neural
  networks
Flow reconstruction in time-varying geometries using graph neural networks
Bogdan A. Danciu
Vito A. Pagone
Benjamin Böhm
Marius Schmidt
Christos E. Frouzakis
AI4CE
26
1
0
13 Nov 2024
Recipe for a General, Powerful, Scalable Graph Transformer
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek
Mikhail Galkin
Vijay Prakash Dwivedi
Anh Tuan Luu
Guy Wolf
Dominique Beaini
125
576
0
25 May 2022
Rethinking Graph Transformers with Spectral Attention
Rethinking Graph Transformers with Spectral Attention
Devin Kreuzer
Dominique Beaini
William L. Hamilton
Vincent Létourneau
Prudencio Tossou
102
543
0
07 Jun 2021
A Generalization of Transformer Networks to Graphs
A Generalization of Transformer Networks to Graphs
Vijay Prakash Dwivedi
Xavier Bresson
AI4CE
107
758
0
17 Dec 2020
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
673
41,430
0
22 Oct 2020
Rethinking Attention with Performers
Rethinking Attention with Performers
K. Choromanski
Valerii Likhosherstov
David Dohan
Xingyou Song
Andreea Gane
...
Afroz Mohiuddin
Lukasz Kaiser
David Belanger
Lucy J. Colwell
Adrian Weller
186
1,600
0
30 Sep 2020
Transformers are RNNs: Fast Autoregressive Transformers with Linear
  Attention
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Angelos Katharopoulos
Apoorv Vyas
Nikolaos Pappas
Franccois Fleuret
203
1,786
0
29 Jun 2020
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
490
950
0
02 Mar 2020
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
257
7,695
0
01 Oct 2018
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
481
20,233
0
30 Oct 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
786
132,363
0
12 Jun 2017
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
514
15,319
0
07 Jun 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
665
29,156
0
09 Sep 2016
Effective Approaches to Attention-based Neural Machine Translation
Effective Approaches to Attention-based Neural Machine Translation
Thang Luong
Hieu H. Pham
Christopher D. Manning
413
7,969
0
17 Aug 2015
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau
Kyunghyun Cho
Yoshua Bengio
AIMat
578
27,327
0
01 Sep 2014
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