Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks

3 August 2023

Papers citing "Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks"

7 / 7 papers shown

Title
On latent dynamics learning in nonlinear reduced order modeling N. Farenga S. Fresca Simone Brivio Andrea Manzoni AI4CE 22 1 0 27 Aug 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
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 38 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
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions Masanobu Horie Naoto Mitsume PINN AI4CE 19 23 0 24 May 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 44 39 0 16 May 2022
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 252 1,394 0 01 Dec 2016