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Found 6 papers, 4 papers with code
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces.
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines.
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications.
Learning Lagrangian Fluid Mechanics with E($3$)-Equivariant Graph Neural Networks
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts.
E($3$) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts.
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences.
