Lagrangian Fluid Mechanics

We identify particle clustering originating from tensile instabilities as one of the primary pitfalls. Based on these insights, we enhance both training and rollout inference of GNN-based simulators with varying components from standard SPH solvers, including pressure, viscous, and external force components.

Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity …

Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks

We introduce E(3)-equivariant GNNs to two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Poiseuille flow. Published at GSI 2023.

Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks

We introduce E(3)-equivariant GNNs to two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Poiseuille flow. Published at GSI 2023.

Boundary Graph Neural Networks for 3D Simulations

We generalize graph neural network based simulations of Lagrangian dynamics to complex boundaries as encountered in daily life engineering setups. Published at AAAI 2023.