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Graph Convolutional Neural Networks for Body Force Prediction

Graph Convolutional Neural Networks for Body Force Prediction

3 December 2020
Francis Ogoke
Kazem Meidani
Amirreza Hashemi
A. Farimani
    GNNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Graph Convolutional Neural Networks for Body Force Prediction"

14 / 14 papers shown
RheOFormer: A generative transformer model for simulation of complex fluids and flows
RheOFormer: A generative transformer model for simulation of complex fluids and flows
Maedeh Saberi
Amir Barati Farimani
Safa Jamali
AI4CE
148
0
0
01 Oct 2025
Benchmarking machine learning models for predicting aerofoil performance
Benchmarking machine learning models for predicting aerofoil performance
Oliver Summerell
Gerardo Aragon-Camarasa
Stephanie Ordonez Sanchez
234
0
0
22 Apr 2025
Machine learning for modelling unstructured grid data in computational physics: a review
Machine learning for modelling unstructured grid data in computational physics: a reviewInformation Fusion (Inf. Fusion), 2025
Sibo Cheng
Marc Bocquet
Weiping Ding
Tobias S. Finn
Rui Fu
...
Yong Zeng
Mingrui Zhang
Hao Zhou
Kewei Zhu
Rossella Arcucci
PINNAI4CE
577
20
0
13 Feb 2025
A review of graph neural network applications in mechanics-related
  domains
A review of graph neural network applications in mechanics-related domains
Yingxue Zhao
Haoran Li
Haosu Zhou
H. Attar
Tobias Pfaff
Nan Li
AI4CE
332
45
0
10 Jul 2024
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
Sunwoong Yang
Ricardo Vinuesa
Namwoo Kang
AI4CE
205
6
0
06 Jun 2024
Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids
  Using Autoencoder Graph Convolutional Networks
Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks
Gabriele Immordino
Andrea Vaiuso
A. Ronch
Marcello Righi
AI4CE
212
4
0
07 May 2024
Finite Volume Features, Global Geometry Representations, and Residual
  Training for Deep Learning-based CFD Simulation
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD SimulationInternational Conference on Machine Learning (ICML), 2023
Loh Sher En Jessica
Naheed Anjum Arafat
Wei Xian Lim
Wai Lee Chan
A. W. Kong
AI4CE
199
3
0
24 Nov 2023
Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing
  Using Generative Deep Diffusion
Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion
Francis Ogoke
Quanliang Liu
Olabode T. Ajenifujah
Alexander J. Myers
Guadalupe Quirarte
Jack L. Beuth
Jonathan A. Malen
A. Farimani
AI4CE
358
20
0
15 Nov 2023
Learning to simulate partially known spatio-temporal dynamics with
  trainable difference operators
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators
Xiang Huang
Zhuoyuan Li
Hongsheng Liu
Zidong Wang
Hongye Zhou
Bin Dong
Bei Hua
AI4TSAI4CE
244
1
0
26 Jul 2023
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in
  Computational Fluid Dynamics
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
Cooper Lorsung
A. Farimani
AI4CE
163
3
0
02 Dec 2022
Deep learning and multi-level featurization of graph representations of
  microstructural data
Deep learning and multi-level featurization of graph representations of microstructural dataComputational Mechanics (Comput. Mech.), 2022
Reese E. Jones
Cosmin Safta
A. Frankel
AI4CE
153
5
0
29 Sep 2022
Transformer for Partial Differential Equations' Operator Learning
Transformer for Partial Differential Equations' Operator Learning
Zijie Li
Kazem Meidani
A. Farimani
439
275
0
26 May 2022
Flow Completion Network: Inferring the Fluid Dynamics from Incomplete
  Flow Information using Graph Neural Networks
Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural NetworksThe Physics of Fluids (Phys. Fluids), 2022
Xiaodong He
Yinan Wang
Juan Li
GNN
203
34
0
10 May 2022
A physics and data co-driven surrogate modeling approach for temperature
  field prediction on irregular geometric domain
A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domainStructural And Multidisciplinary Optimization (SMO), 2022
K. Bao
Wenjuan Yao
Xiaoya Zhang
Wei Peng
Yu Li
AI4CE
250
16
0
15 Mar 2022
1
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