E($3$) Equivariant Graph Neural Networks for Particle-Based Fluid
Mechanics

E( $3$ ) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics

31 March 2023

Artur P. Toshev

Gianluca Galletti

Johannes Brandstetter

Stefan Adami

Nikolaus A. Adams

Papers citing "E($3$) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics"

6 / 6 papers shown

Title
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems Yunyang Li Zaishuo Xia Lin Huang Xinran Wei Han Yang ... Zun Wang Chang-Shu Liu Jia Zhang Bin Shao Mark B. Gerstein 77 0 0 26 Feb 2025
A review of graph neural network applications in mechanics-related domains Yingxue Zhao Haoran Li Haosu Zhou H. Attar Tobias Pfaff Nan Li AI4CE 34 5 0 10 Jul 2024
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 34 43 0 09 Jul 2024
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 26 0 0 16 Oct 2023
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 206 1,240 0 08 Jan 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 223 2,287 0 18 Oct 2020