6 DoF Dynamics : Descrete Element Method (DEM) Simulation Dataset for Learning GNN Surrogate Model

This dataset accompanies the paper:
“Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems” V. Sharma & O. Fink, 2025— arXiv: 2501.07373 : Link to Paper

It provides all 6 Degrees of Freedom Discrete Element Method (DEM) simulation data used for training, validation, andtesting of the Dynami-CAL GraphNet model. Each zipped archive contains time-resolved particle trajectories with explicit boundary conditions and contact parameters. The simulations were performed using the MFiX DEM framework. 

For detailed description refer to : Detailed_Information_on_Data_Structure.pdf