Electrostatic field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields

This repository contains the "Electrostatic field" dataset from the paper

Latent Field Discovery in Interacting Dynamical Systems with Neural FieldsMiltiadis Kofinas, Erik J Bekkers, Naveen Shankar Nagaraja, Efstratios GavvesNeurIPS 2023https://arxiv.org/abs/2310.20679https://github.com/mkofinas/aether

It contains simulations of trajectories of 5 charged particles in 2 dimensions, interacting via Coulomb forces.

Particles move under the influence of 20 immovable and unknown sources, which are shared in the whole dataset.

There are 50,000 simulations for training, 10,000 for validation, and 10,000 for testing. Simulations last for 49 timesteps.

The features comprise positions and velocities of particles, while edges describe the product of pairwise charges. The dataset also contains the positions of the field sources, meant to be used for visualization.

本仓库收录了来自论文《利用神经场探索交互动力学系统的隐场》（Latent Field Discovery in Interacting Dynamical Systems with Neural Fields）的Electrostatic field（静电场）数据集。该论文作者为Miltiadis Kofinas、Erik J Bekkers、Naveen Shankar Nagaraja及Efstratios Gavves，发表于NeurIPS 2023，相关链接为：https://arxiv.org/abs/2310.20679、https://github.com/mkofinas/aether。
该数据集包含二维空间中5个带电粒子经由库仑力相互作用的运动轨迹仿真数据。粒子在20个固定且未知的场源作用下运动，所有仿真样本均共享这些场源。
数据集共包含50000组训练样本、10000组验证样本及10000组测试样本，每组仿真涵盖49个时间步长。
数据特征包含粒子的位置与速度信息，边（edges）用于表征两两粒子间的电荷乘积；数据集同时附带场源的位置信息，可用于可视化任务。