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A graph convolutional autoencoder approach to model order reduction for
  parametrized PDEs
v1v2 (latest)

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Journal of Computational Physics (JCP), 2023
15 May 2023
F. Pichi
B. Moya
J. Hesthaven
    AI4CE
ArXiv (abs)PDFHTMLGithub (37★)

Papers citing "A graph convolutional autoencoder approach to model order reduction for parametrized PDEs"

26 / 26 papers shown
Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition
Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition
Yuanhong Chen
F. Pichi
Zhen Gao
G. Rozza
AI4TSAI4CE
140
1
0
28 Nov 2025
Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model
Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model
Yanlai Chen
Yajie Ji
Zhenli Xu
143
0
0
14 Sep 2025
Variational Rank Reduction Autoencoders for Generative
Variational Rank Reduction Autoencoders for Generative
Alicia Tierz
Jad Mounayer
B. Moya
Francisco Chinesta
DRLAI4CE
277
2
0
10 Sep 2025
Autoencoder-based non-intrusive model order reduction in continuum mechanics
Autoencoder-based non-intrusive model order reduction in continuum mechanics
Jannick Kehls
Ellen Kuhl
T. Brepols
K. Linka
H. Holthusen
AI4CE
132
1
0
02 Sep 2025
Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Pucheng Tang
Hongqiao Wang
Wenzhou Lin
Qian Chen
Heng Yong
157
1
0
01 Sep 2025
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Simone Brivio
Nicola Rares Franco
225
1
0
13 Jun 2025
A discrete physics-informed training for projection-based reduced order models with neural networks
A discrete physics-informed training for projection-based reduced order models with neural networks
N. Sibuet
S. A. D. Parga
J. R. Bravo
R. Rossi
387
4
0
31 Mar 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
605
25
0
13 Feb 2025
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2024
Shivam Barwey
Pinaki Pal
Saumil Patel
Riccardo Balin
Bethany Lusch
V. Vishwanath
R. Maulik
R. Balakrishnan
AI4CE
784
12
0
12 Sep 2024
Data-driven identification of latent port-Hamiltonian systems
Data-driven identification of latent port-Hamiltonian systems
J. Rettberg
Jonas Kneifl
Julius Herb
Patrick Buchfink
Jörg Fehr
B. Haasdonk
PINN
410
6
0
15 Aug 2024
Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction
Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction
Hrishikesh Viswanath
Yue Chang
Julius Berner
Julius Berner
Peter Yichen Chen
Aniket Bera
AI4CE
644
4
0
04 Jul 2024
Sparsifying dimensionality reduction of PDE solution data with Bregman
  learning
Sparsifying dimensionality reduction of PDE solution data with Bregman learningSIAM Journal on Scientific Computing (SISC), 2024
T. J. Heeringa
Christoph Brune
Mengwu Guo
189
2
0
18 Jun 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
230
7
0
06 Jun 2024
GFN: A graph feedforward network for resolution-invariant reduced
  operator learning in multifidelity applications
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
Oisín M. Morrison
F. Pichi
J. Hesthaven
AI4CE
322
18
0
05 Jun 2024
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
Masanobu Horie
Naoto Mitsume
AI4CE
258
17
0
25 May 2024
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced
  order models for nonlinear parametrized PDEs
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEsComputer Methods in Applied Mechanics and Engineering (CMAME), 2024
Simone Brivio
S. Fresca
Andrea Manzoni
AI4CE
382
14
0
14 May 2024
A comparison of Single- and Double-generator formalisms for
  Thermodynamics-Informed Neural Networks
A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks
Pau Urdeitx
Ic´ıar Alfaro
David González
Francisco Chinesta
Elías Cueto
AI4CE
240
6
0
01 Apr 2024
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash
  Simulations Using Graph Convolutional Neural Networks
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
Jonas Kneifl
Jörg Fehr
Steven L. Brunton
J. Nathan Kutz
AI4CE
291
1
0
14 Feb 2024
A practical existence theorem for reduced order models based on
  convolutional autoencoders
A practical existence theorem for reduced order models based on convolutional autoencoders
N. R. Franco
Simone Brugiapaglia
AI4CE
376
11
0
01 Feb 2024
Polytopic Autoencoders with Smooth Clustering for Reduced-order
  Modelling of Flows
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
Jan Heiland
Yongho Kim
AI4CE
254
6
0
19 Jan 2024
Mitigating distribution shift in machine learning-augmented hybrid simulation
Mitigating distribution shift in machine learning-augmented hybrid simulationSIAM Journal on Scientific Computing (SISC), 2024
Jiaxi Zhao
Qianxiao Li
323
5
0
17 Jan 2024
Generating synthetic data for neural operators
Generating synthetic data for neural operatorsSMAI Journal of Computational Mathematics (SMAI-JCM), 2024
Erisa Hasani
Rachel A. Ward
AI4CE
672
13
0
04 Jan 2024
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
M. Khamlich
F. Pichi
G. Rozza
427
5
0
26 Aug 2023
Branched Latent Neural Maps
Branched Latent Neural MapsComputer Methods in Applied Mechanics and Engineering (CMAME), 2023
M. Salvador
Alison Lesley Marsden
353
7
0
04 Aug 2023
Deep Learning-based surrogate models for parametrized PDEs: handling
  geometric variability through graph neural networks
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networksChaos (Chaos), 2023
N. R. Franco
S. Fresca
Filippo Tombari
Andrea Manzoni
AI4CE
264
38
0
03 Aug 2023
An Implicit GNN Solver for Poisson-like problems
An Implicit GNN Solver for Poisson-like problemsComputers and Mathematics with Applications (CMA), 2023
Matthieu Nastorg
M. Bucci
T. Faney
J. Gratien
Guillaume Charpiat
Marc Schoenauer
AI4CE
502
9
0
06 Feb 2023
1
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