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Found 4 papers, 2 papers with code
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.
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks
In this work, physics-informed neural networks are applied to incompressible two-phase flow problems.
