SamuelRx/Elastic-Rod-Dataset

该数据集包含从离散弹性杆（DER）模拟生成的合成数据，这是一种物理模型，用于表示可变形细长结构，如发丝、绳索、电缆和弹性纤维。数据集在物理模拟过程中逐帧生成，并存储每个杆顶点的几何、运动学和动力学属性。该数据集的主要目标是支持涉及图神经网络（GNN）、物理信息学习、动力学预测、物理回归和可变形对象模拟的机器学习研究。每个样本对应于特定模拟帧中的一个顶点。This dataset contains synthetic data generated from Discrete Elastic Rods (DER) simulations, a physical model used to represent deformable slender structures such as hair strands, ropes, cables, and elastic fibers. The dataset was generated frame-by-frame during physical simulations and stores geometric, kinematic, and dynamic properties for each rod vertex. The primary objective of this dataset is to support machine learning research involving Graph Neural Networks (GNNs), physics-informed learning, dynamics prediction, physical regression, and deformable object simulation. Each sample corresponds to a vertex at a specific simulation frame.