Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning

13 February 2023

Papers citing "Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning"

5 / 5 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 96 0 0 12 Sep 2024
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 19 16 0 03 Aug 2023
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F. Pichi B. Moya J. Hesthaven AI4CE 25 51 0 15 May 2023
An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations F. Bonnet Jocelyn Ahmed Mazari T. Munzer P. Yser Patrick Gallinari AI4CE 51 10 0 29 Jun 2022
A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling A. Gruber M. Gunzburger L. Ju Zhu Wang GNN 24 62 0 05 Oct 2021