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

15 May 2023

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

24 / 24 papers shown

Title
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks Shivam Barwey Pinaki Pal Saumil Patel Riccardo Balin Bethany Lusch V. Vishwanath R. Maulik R. Balakrishnan AI4CE 93 0 0 12 Sep 2024
Data-driven identification of latent port-Hamiltonian systems J. Rettberg Jonas Kneifl Julius Herb Patrick Buchfink Jörg Fehr B. Haasdonk PINN 14 2 0 15 Aug 2024
Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs Hrishikesh Viswanath Yue Chang Julius Berner Peter Yichen Chen Aniket Bera AI4CE 58 2 0 04 Jul 2024
Sparsifying dimensionality reduction of PDE solution data with Bregman learning T. J. Heeringa Christoph Brune Mengwu Guo 19 0 0 18 Jun 2024
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction Sunwoong Yang Ricardo Vinuesa Namwoo Kang AI4CE 28 4 0 06 Jun 2024
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications Oisín M. Morrison F. Pichi J. Hesthaven AI4CE 19 1 0 05 Jun 2024
Neural empirical interpolation method for nonlinear model reduction Max Hirsch F. Pichi J. Hesthaven 27 1 0 05 Jun 2024
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance Masanobu Horie Naoto Mitsume AI4CE 19 5 0 25 May 2024
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs Simone Brivio S. Fresca Andrea Manzoni AI4CE 28 6 0 14 May 2024
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 28 1 0 01 Apr 2024
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 20 1 0 14 Feb 2024
A practical existence theorem for reduced order models based on convolutional autoencoders N. R. Franco Simone Brugiapaglia AI4CE 16 4 0 01 Feb 2024
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows Jan Heiland Yongho Kim AI4CE 11 2 0 19 Jan 2024
Mitigating distribution shift in machine learning-augmented hybrid simulation Jiaxi Zhao Qianxiao Li 19 2 0 17 Jan 2024
Generating synthetic data for neural operators Erisa Hasani Rachel A. Ward AI4CE 37 7 0 04 Jan 2024
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 13 5 0 26 Aug 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 22 4 0 04 Aug 2023
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks N. R. Franco S. Fresca Filippo Tombari Andrea Manzoni AI4CE 16 16 0 03 Aug 2023
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks Luca Pegolotti Martin R. Pfaller Natalia L. Rubio Ke Ding Rita Brugarolas Brufau Eric F. Darve Alison L. Marsden AI4CE 41 31 0 13 Mar 2023
An Implicit GNN Solver for Poisson-like problems Matthieu Nastorg M. Bucci T. Faney J. Gratien Guillaume Charpiat Marc Schoenauer AI4CE 26 2 0 06 Feb 2023
A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling A. Gruber M. Gunzburger L. Ju Zhu Wang GNN 21 62 0 05 Oct 2021
Enhancing Computational Fluid Dynamics with Machine Learning Ricardo Vinuesa Steven L. Brunton AI4CE 98 351 0 05 Oct 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 163 1,095 0 27 Apr 2021
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,801 0 25 Nov 2016