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Learning Mechanically Driven Emergent Behavior with Message Passing
  Neural Networks
v1v2v3 (latest)

Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks

3 February 2022
Peerasait Prachaseree
Emma Lejeune
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks"

34 / 34 papers shown
Title
Spiderweb nanomechanical resonators via Bayesian optimization: inspired
  by nature and guided by machine learning
Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning
Dongil Shin
Andrea Cupertino
M. H. J. D. Jong
P. Steeneken
Miguel A. Bessa
R. Norte
44
66
0
10 Aug 2021
Predicting Mechanically Driven Full-Field Quantities of Interest with
  Deep Learning-Based Metamodels
Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels
S. Mohammadzadeh
Emma Lejeune
AI4CE
67
28
0
24 Jul 2021
Predicting the Mechanical Properties of Biopolymer Gels Using Neural
  Networks Trained on Discrete Fiber Network Data
Predicting the Mechanical Properties of Biopolymer Gels Using Neural Networks Trained on Discrete Fiber Network Data
Yue Leng
Vahidullah Tac
S. Calve
A. B. Tepole
82
32
0
23 Jan 2021
Bayesian neural networks for weak solution of PDEs with uncertainty
  quantification
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
Xiaoxuan Zhang
K. Garikipati
AI4CE
81
12
0
13 Jan 2021
Exploring the potential of transfer learning for metamodels of
  heterogeneous material deformation
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
Emma Lejeune
Bill Zhao
AI4CE
46
20
0
28 Oct 2020
Deep Autoencoder based Energy Method for the Bending, Vibration, and
  Buckling Analysis of Kirchhoff Plates
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates
X. Zhuang
Hongwei Guo
N. Alajlan
Timon Rabczuk
AI4CE
57
319
0
09 Oct 2020
Learning Mesh-Based Simulation with Graph Networks
Learning Mesh-Based Simulation with Graph Networks
Tobias Pfaff
Meire Fortunato
Alvaro Sanchez-Gonzalez
Peter W. Battaglia
AI4CE
84
806
0
07 Oct 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
Discovering Symbolic Models from Deep Learning with Inductive Biases
M. Cranmer
Alvaro Sanchez-Gonzalez
Peter W. Battaglia
Rui Xu
Kyle Cranmer
D. Spergel
S. Ho
AI4CE
86
483
0
19 Jun 2020
Learning to Simulate Complex Physics with Graph Networks
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez
Jonathan Godwin
Tobias Pfaff
Rex Ying
J. Leskovec
Peter W. Battaglia
PINNAI4CE
148
1,104
0
21 Feb 2020
Geometric deep learning for computational mechanics Part I: Anisotropic
  Hyperelasticity
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity
Nikolaos N. Vlassis
R. Ma
WaiChing Sun
AI4CE
57
175
0
08 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
583
42,677
0
03 Dec 2019
Learning Symbolic Physics with Graph Networks
Learning Symbolic Physics with Graph Networks
M. Cranmer
Rui Xu
Peter W. Battaglia
S. Ho
PINNAI4CE
272
86
0
12 Sep 2019
GNNExplainer: Generating Explanations for Graph Neural Networks
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying
Dylan Bourgeois
Jiaxuan You
Marinka Zitnik
J. Leskovec
LLMAG
155
1,334
0
10 Mar 2019
Fast Graph Representation Learning with PyTorch Geometric
Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey
J. E. Lenssen
3DHGNN3DPC
256
4,371
0
06 Mar 2019
A Survey of the Recent Architectures of Deep Convolutional Neural
  Networks
A Survey of the Recent Architectures of Deep Convolutional Neural Networks
Asifullah Khan
A. Sohail
Umme Zahoora
Aqsa Saeed Qureshi
OOD
132
2,311
0
17 Jan 2019
Graph Neural Networks: A Review of Methods and Applications
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou
Ganqu Cui
Shengding Hu
Zhengyan Zhang
Cheng Yang
Zhiyuan Liu
Lifeng Wang
Changcheng Li
Maosong Sun
AI4CEGNN
1.1K
5,551
0
20 Dec 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
301
905
0
07 Jun 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CENAI
773
3,132
0
04 Jun 2018
Dynamic Graph CNN for Learning on Point Clouds
Dynamic Graph CNN for Learning on Point Clouds
Yue Wang
Yongbin Sun
Ziwei Liu
Sanjay E. Sarma
M. Bronstein
Justin Solomon
GNN3DPC
265
6,179
0
24 Jan 2018
On Calibration of Modern Neural Networks
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
UQCV
299
5,877
0
14 Jun 2017
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric
  Space
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
C. Qi
L. Yi
Hao Su
Leonidas Guibas
3DPC3DV
378
11,164
0
07 Jun 2017
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti
M. Bronstein
Xavier Bresson
GNN
196
522
0
22 Apr 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
600
7,500
0
04 Apr 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
850
5,849
0
05 Dec 2016
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
440
1,824
0
25 Nov 2016
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
821
3,302
0
24 Nov 2016
Automatic chemical design using a data-driven continuous representation
  of molecules
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli
Jennifer N. Wei
David Duvenaud
José Miguel Hernández-Lobato
Benjamín Sánchez-Lengeling
Dennis Sheberla
J. Aguilera-Iparraguirre
Timothy D. Hirzel
Ryan P. Adams
Alán Aspuru-Guzik
3DV
185
2,945
0
07 Oct 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,641
0
10 Dec 2015
ShapeNet: An Information-Rich 3D Model Repository
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang
Thomas Funkhouser
Leonidas Guibas
Pat Hanrahan
Qi-Xing Huang
...
Shuran Song
Hao Su
Jianxiong Xiao
L. Yi
Feng Yu
3DV
176
5,538
0
09 Dec 2015
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud
D. Maclaurin
J. Aguilera-Iparraguirre
Rafael Gómez-Bombarelli
Timothy D. Hirzel
Alán Aspuru-Guzik
Ryan P. Adams
GNN
239
3,356
0
30 Sep 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
471
43,357
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.2K
150,433
0
22 Dec 2014
scikit-image: Image processing in Python
scikit-image: Image processing in Python
Stéfan van der Walt
Johannes L. Schonberger
Juan Nunez-Iglesias
François Boulogne
Joshua D. Warner
Neil Yager
Emmanuelle Gouillart
Tony Yu
the scikit-image contributors
SSegGPVLM
206
4,351
0
23 Jul 2014
Multi-column Deep Neural Networks for Image Classification
Multi-column Deep Neural Networks for Image Classification
D. Ciresan
U. Meier
Jürgen Schmidhuber
183
3,945
0
13 Feb 2012
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