Hamiltonian System Data

该数据集包含了用于训练哈密顿神经网络的状态变量样本和向量场，旨在回归哈密顿量和其向量场。该数据集是从具有特定参数的哈密顿系统生成的，用于实证研究不同模型在回归哈密顿量和向量场方面的性能。相关的代码和结果可在GitHub上获取。数据规模为：对于每个哈密顿系统，均匀随机生成了512个状态变量和向量场的样本。任务目标是进行哈密顿量和向量场的回归分析。

This dataset comprises state variable samples and vector fields for training Hamiltonian neural networks, with the core goal of regressing the Hamiltonian and its corresponding vector fields. It is generated from Hamiltonian systems with specified parameters, and is designed for empirical investigations into the performance of various models when conducting regression of the Hamiltonian and vector fields. Relevant code and results are available on GitHub. The dataset scale is as follows: For each Hamiltonian system, 512 samples of state variables and vector fields are uniformly randomly generated. The task objective is to perform regression analysis of the Hamiltonian and vector fields.