Training, validation, testing data, and trained model

The dataset includes the training/validation/testing data for the graph neural network (GNN)-based simulator (GNS) used in Choi and Kumar (2023) "Graph Neural Network-based Surrogate Model for Granular Flows" cited in the Related Work. Graph neural network (GNN)-based simulator (GNS) is a generalizable, efficient, and accurate machine learning (ML)-based surrogate simulator that uses Graph Neural Networks (GNNs) originally introduced in the paper "Learning to Simulate Complex Physics with Graph Networks" by DeepMind (see the Related work). GNS is a viable surrogate for numerical methods such as Material Point Method, Smooth Particle Hydrodynamics and Computational Fluid dynamics and can be extended to simulate natural hazards. GNS can handle complex boundary conditions and multi-material interactions. We improved GNS to be able to exploit distributed data parallelism to achieve fast multi-GPU training. This improved version of GNS is published in GitHub "Graph Network Simulator (GNS) and MeshNet" (see the Related Work). We investigate the performance of GNS in learning to simulate complex granular flow problems: "BarrierInteraction", "ColumnCollapseSimple", and "ColumnCollapseFrictional". The details of these granular flow problems are explained in Choi and Kumar (2023). The GNS is trained on the `train.npz`, and validated and tested on the `validation.npz` and `test.npz` uploaded in this dataset. Detailed instructions on how to train and test the GNS are explained in `README.md` file in the GitHub repository https://github.com/geoelements/gns in the Related Work. The trained model is also included in this dataset. The result shows that GNS can accurately simulate the granular flow dynamics faster than high-fidelity simulators, and is generalizable to other configurations not seen during the training. More information about the result can be found in Choi and Kumar (2023).