Simulating Liquids with Graph Networks

14 March 2022

Papers citing "Simulating Liquids with Graph Networks"

9 / 9 papers shown

Title
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
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
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite Stefania Costantini Gianluca Galletti Fabian Fritz Stefan Adami Nikolaus A. Adams 40 13 0 28 Sep 2023
Importance of equivariant and invariant symmetries for fluid flow modeling Varun Shankar Shivam Barwey Zico Kolter R. Maulik V. Viswanathan AI4CE 22 4 0 03 May 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 13 5 0 31 Mar 2023
Invariant preservation in machine learned PDE solvers via error correction N. McGreivy Ammar Hakim AI4CE PINN 26 8 0 28 Mar 2023
Thermodynamics of learning physical phenomena Elías Cueto Francisco Chinesta AI4CE 25 22 0 26 Jul 2022
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 188 83 0 12 Sep 2019
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