On the Relationships between Graph Neural Networks for the Simulation of
Physical Systems and Classical Numerical Methods

On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods

31 March 2023

Artur P. Toshev

Nikolaus A. Adams

Papers citing "On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods"

7 / 7 papers shown

Title
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework Artur P. Toshev Harish D. Ramachandran Jonas A. Erbesdobler Gianluca Galletti Johannes Brandstetter Nikolaus A. Adams 46 7 0 07 Mar 2024
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics Artur P. Toshev Jonas A. Erbesdobler Nikolaus A. Adams Johannes Brandstetter AI4CE 46 4 0 09 Feb 2024
REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics Mario Lino Stati Fotiadis Anil A. Bharath C. Cantwell AI4CE 19 3 0 05 May 2022
Grid-Free Monte Carlo for PDEs with Spatially Varying Coefficients Rohan Sawhney Dario Seyb Wojciech Jarosz Keenan Crane 32 33 0 31 Jan 2022
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 220 2,287 0 18 Oct 2020
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni De Fabritiis Frank Noé C. Clementi AI4CE 40 158 0 22 Jul 2020