Boundary Graph Neural Networks for 3D Simulations
This addresses the challenge of integrating heterogeneous triangularized boundaries into machine learning for industrial applications like hoppers and mixers, offering a novel solution for modeling particle-boundary interactions.
The paper tackled the problem of efficiently representing geometric boundaries in 3D simulations by introducing Boundary Graph Neural Networks (BGNNs), which accurately reproduced 3D granular flows within simulation uncertainties over hundreds of thousands of timesteps without handcrafted conditions.
The abundance of data has given machine learning considerable momentum in natural sciences and engineering, though modeling of physical processes is often difficult. A particularly tough problem is the efficient representation of geometric boundaries. Triangularized geometric boundaries are well understood and ubiquitous in engineering applications. However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation. In this work, we introduce an effective theory to model particle-boundary interactions, which leads to our new Boundary Graph Neural Networks (BGNNs) that dynamically modify graph structures to obey boundary conditions. The new BGNNs are tested on complex 3D granular flow processes of hoppers, rotating drums and mixers, which are all standard components of modern industrial machinery but still have complicated geometry. BGNNs are evaluated in terms of computational efficiency as well as prediction accuracy of particle flows and mixing entropies. BGNNs are able to accurately reproduce 3D granular flows within simulation uncertainties over hundreds of thousands of simulation timesteps. Most notably, in our experiments, particles stay within the geometric objects without using handcrafted conditions or restrictions.