Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs

4 July 2024

Papers citing "Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs"

7 / 57 papers shown

Title
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 163 727 0 07 Mar 2020
Learning to Simulate Complex Physics with Graph Networks Alvaro Sanchez-Gonzalez Jonathan Godwin Tobias Pfaff Rex Ying J. Leskovec Peter W. Battaglia PINN AI4CE 129 1,080 0 21 Feb 2020
Generalization and Representational Limits of Graph Neural Networks Vikas Garg Stefanie Jegelka Tommi Jaakkola GNN 94 312 0 14 Feb 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 204 2,108 0 08 Oct 2019
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 67 1,245 0 27 Dec 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 84 2,059 0 24 Aug 2017
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 508 1,407 0 01 Dec 2016