Boundary Graph Neural Networks for 3D Simulations
Authors: Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The new BGNNs are tested on complex 3D granular flow processes of hoppers, rotating drums and mixers...BGNNs are evaluated in terms of computational efficiency as well as prediction accuracy of particle flows and mixing entropies. In this work, training, validation, and test data are generated by LIGGGHTS modeling particle trajectories within different machinery designs. |
| Researcher Affiliation | Collaboration | Andreas Mayr1, , Sebastian Lehner1, Arno Mayrhofer2, Christoph Kloss2, Sepp Hochreiter1,3, Johannes Brandstetter1, ,* 1 ELLIS Unit Linz & LIT AI Lab, Johannes Kepler University Linz, Linz, Austria 2 DCS Computing Gmb H, Linz, Austria 3 Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria *now at Microsoft Research AI4Science |
| Pseudocode | Yes | Algorithm 1: BGNN: Dynamic Graph Message Passing |
| Open Source Code | Yes | Code is available at https://ml-jku.github.io/bgnn/ |
| Open Datasets | No | In this work, training, validation, and test data are generated by LIGGGHTS modeling particle trajectories within different machinery designs. No specific link or citation for the generated dataset is provided. |
| Dataset Splits | Yes | In this work, training, validation, and test data are generated by LIGGGHTS modeling particle trajectories within different machinery designs. |
| Hardware Specification | Yes | LIGGGGHTS simulation is run on a CPU AMD EPYC 7H12, BGNN forward pass is run on a GPU NVIDIA A100. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or other libraries) are mentioned in the text. |
| Experiment Setup | Yes | We use 3 to 10 message passing layers, with 128 and 512 nodes for intermediate node and edge representation. The cut-off radii strongly depend on the particle size. We use cut-off radii of 0.02 and 0.008 for rotating drum and hopper, respectively. Cut-off radii have been treated as hyperparameters of our model. |