Modeling the structure and events of the physical world constitutes a fundamental objective of neural networks. Among the diverse approaches, Graph Network Simulators (GNS) have emerged as the leading method for modeling physical phenomena, owing to their low computational cost and high accuracy. The datasets employed for training and evaluating physical simulation techniques are typically generated by researchers themselves, often resulting in limited data volume and quality. Consequently, this poses challenges in accurately assessing the performance of these methods. In response to this, we have constructed a high-quality physical simulation dataset encompassing 1D, 2D, and 3D scenes, along with more trajectories and time-steps compared to existing datasets. Furthermore, our 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 multi-body dynamics, facilitating a more realistic simulation of the physical world. Utilizing our high-quality dataset, we conducted a systematic evaluation of various existing GNS methods. Our dataset is accessible for download at https://github.com/Sherlocktein/MBDS, offering a valuable resource for researchers to enhance the training and evaluation of their methodologies.

翻译：对物理世界的结构与事件进行建模是神经网络的基本目标之一。在众多方法中，图网络模拟器（GNS）因其计算成本低、精度高而成为模拟物理现象的主流方法。目前用于训练和评估物理仿真技术的数据集通常由研究者自行生成，往往存在数据量有限、质量参差不齐的问题，从而难以准确评估这些方法的性能。为此，我们构建了一个高质量的物理仿真数据集，涵盖一维、二维和三维场景，且相比现有数据集包含更多轨迹与时间步。此外，本工作的突出贡献在于开发了八个完整场景，显著提升了数据集的全面性。我们数据集的一个关键特点是包含精确的多体动力学信息，有助于更真实地模拟物理世界。利用这一高质量数据集，我们对多种现有GNS方法进行了系统评估。本数据集可通过 https://github.com/Sherlocktein/MBDS 下载，为研究者改进方法的训练与评估提供了宝贵资源。