MBDS: A MultiBody Dynamics Simulation Dataset for Graph Networks Simulators
Sheng Yang, Tao Peng, Zeyu Li, Fengge Wu, Junsuo Zhao · 2025
Modeling the structure and events of the physical world constitutes a fundamental objective of neural networks. Graph Network Simulators (GNS) have become the leading approach due to their computational efficiency and accuracy. However, the scarcity of comprehensive datasets limits their full potential, especially for multibody dynamics, crucial in real-world applications. In response to this, a high-quality physical simulation dataset has been constructed, encompassing 1D, 2D, and 3D scenes, along with more trajectories and time-steps compared to existing datasets. Furthermore, this work distinguishes itself by developing eight complete scenes, significantly enhancing the dataset’s comprehensiveness. A key feature of our dataset is the inclusion of precise multibody dynamics, facilitating a more realistic simulation of the physical world. Using this high-quality dataset, a systematic evaluation of various existing GNS methods has been conducted.