Hamiltonian Graph Networks with ODE Integrators

27 September 2019

Alvaro Sanchez-Gonzalez

Papers citing "Hamiltonian Graph Networks with ODE Integrators"

48 / 48 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 143 1 0 29 Mar 2025
Learning System Dynamics without Forgetting Xikun Zhang Dongjin Song Yushan Jiang Yixin Chen Dacheng Tao AI4CE 37 2 0 30 Jun 2024
Graph Neural Reaction Diffusion Models Moshe Eliasof Eldad Haber Eran Treister DiffM AI4CE 35 2 0 16 Jun 2024
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications Jiaqi Han Jiacheng Cen Liming Wu Zongzhao Li Xiangzhe Kong ... Zhewei Wei Deli Zhao Yu Rong Wenbing Huang Wenbing Huang AI4CE 34 20 0 01 Mar 2024
Equivariant Graph Neural Operator for Modeling 3D Dynamics Minkai Xu Jiaqi Han Aaron Lou Jean Kossaifi Arvind Ramanathan Kamyar Azizzadenesheli J. Leskovec Stefano Ermon A. Anandkumar AI4CE 29 16 0 19 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 34 1 0 11 Jul 2023
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators Haakon Noren Sølve Eidnes E. Celledoni 21 3 0 06 Jun 2023
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods Artur P. Toshev Ludger Paehler A. Panizza Nikolaus A. Adams AI4CE PINN 11 5 0 31 Mar 2023
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning Chenxin Xu R. Tan Yuhong Tan Siheng Chen Yu Wang Xinchao Wang Yanfeng Wang 30 96 0 20 Mar 2023
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems A. Thangamuthu Gunjan Kumar S. Bishnoi Ravinder Bhattoo N. M. A. Krishnan Sayan Ranu AI4CE PINN 32 22 0 10 Nov 2022
Thermodynamics-informed neural networks for physically realistic mixed reality Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto PINN AI4CE 17 16 0 24 Oct 2022
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics L. Prantl Benjamin Ummenhofer V. Koltun Nils Thuerey AI4CE PINN 26 29 0 12 Oct 2022
Exact conservation laws for neural network integrators of dynamical systems E. Müller PINN 41 12 0 23 Sep 2022
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan AI4CE 28 17 0 23 Sep 2022
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan PINN AI4CE 31 20 0 03 Sep 2022
Constants of motion network M. F. Kasim Yi Heng Lim 26 4 0 22 Aug 2022
Lagrangian Density Space-Time Deep Neural Network Topology B. Bishnoi PINN 22 1 0 30 Jun 2022
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition Yuelin Wang Kai Yi Xinliang Liu Yu Guang Wang Shi Jin 16 33 0 11 Jun 2022
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks Mario Lino Stathi Fotiadis Anil A. Bharath C. Cantwell AI4CE 11 12 0 05 May 2022
Equivariant Graph Mechanics Networks with Constraints Wen-bing Huang J. Han Yu Rong Tingyang Xu Fuchun Sun Junzhou Huang AI4CE 33 79 0 12 Mar 2022
Thermodynamics-informed graph neural networks Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto AI4CE PINN 27 31 0 03 Mar 2022
Learning Neural Hamiltonian Dynamics: A Methodological Overview Zhijie Chen Mingquan Feng Junchi Yan H. Zha AI4CE 19 15 0 28 Feb 2022
Learning to Simulate Unseen Physical Systems with Graph Neural Networks Ce Yang Weihao Gao Di Wu Chong-Jun Wang PINN AI4CE 22 4 0 28 Jan 2022
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately A. Sosanya S. Greydanus PINN AI4CE 41 27 0 25 Jan 2022
Constraint-based graph network simulator Yulia Rubanova Alvaro Sanchez-Gonzalez Tobias Pfaff Peter W. Battaglia PINN AI4CE 27 28 0 16 Dec 2021
Physics-enhanced Neural Networks in the Small Data Regime Jonas Eichelsdörfer Sebastian Kaltenbach P. Koutsourelakis AI4CE PINN 12 5 0 19 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 27 28 0 09 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 28 93 0 02 Nov 2021
One-Shot Transfer Learning of Physics-Informed Neural Networks Shaan Desai M. Mattheakis H. Joy P. Protopapas Stephen J. Roberts PINN AI4CE 16 58 0 21 Oct 2021
An Extensible Benchmark Suite for Learning to Simulate Physical Systems Karl Otness Arvi Gjoka Joan Bruna Daniele Panozzo Benjamin Peherstorfer T. Schneider Denis Zorin 24 23 0 09 Aug 2021
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems Shaan Desai M. Mattheakis David Sondak P. Protopapas Stephen J. Roberts AI4CE 25 43 0 16 Jul 2021
Machine learning structure preserving brackets for forecasting irreversible processes Kookjin Lee Nathaniel Trask P. Stinis AI4CE 39 42 0 23 Jun 2021
GRAND: Graph Neural Diffusion B. Chamberlain J. Rowbottom Maria I. Gorinova Stefan Webb Emanuele Rossi M. Bronstein GNN 24 253 0 21 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 24 130 0 11 Jun 2021
Adaptable Hamiltonian neural networks Chen-Di Han Bryan Glaz Mulugeta Haile Y. Lai AI4TS 22 25 0 25 Feb 2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs Juntang Zhuang Nicha Dvornek S. Tatikonda James S. Duncan 22 49 0 09 Feb 2021
Learning Physical Constraints with Neural Projections Shuqi Yang Xingzhe He Bo Zhu 3DV AI4CE 33 25 0 23 Jun 2020
Lipschitz Recurrent Neural Networks N. Benjamin Erichson Omri Azencot A. Queiruga Liam Hodgkinson Michael W. Mahoney 30 107 0 22 Jun 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 130 424 0 10 Mar 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 103 49 0 27 Feb 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data Marc Finzi Samuel Stanton Pavel Izmailov A. Wilson 17 316 0 25 Feb 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 51 1,047 0 21 Feb 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 16 21 0 11 Jan 2020
Graph Neural Ordinary Differential Equations Michael Poli Stefano Massaroli Junyoung Park Atsushi Yamashita Hajime Asama Jinkyoo Park AI4CE 42 154 0 18 Nov 2019
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 24 0 0 22 Oct 2019
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 146 219 0 29 Sep 2019
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016
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 280 1,400 0 01 Dec 2016