Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems

10 November 2022

Papers citing "Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems"

7 / 7 papers shown

Title
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks Z. Liu Xiaoda Wang Bohan Wang Zijie Huang Carl Yang Wei-dong Jin AI4TS AI4CE 41 0 0 29 Mar 2025
Towards Effective and General Graph Unlearning via Mutual Evolution Xunkai Li Yulin Zhao Zhengyu Wu Wentao Zhang Ronghua Li Guoren Wang MU 12 13 0 22 Jan 2024
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks S. Bishnoi Ravinder Bhattoo J. Jayadeva Sayan Ranu N. M. A. Krishnan PINN AI4CE 11 1 0 11 Jul 2023
Global Counterfactual Explainer for Graph Neural Networks Mert Kosan Zexi Huang Sourav Medya Sayan Ranu Ambuj K. Singh 11 47 0 21 Oct 2022
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty 50 34 0 12 Feb 2021
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 115 364 0 10 Mar 2020
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