
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.
Physics simulations, Trajectory forecasting, Equivariance, Neural fields, Interacting dynamical systems, Graph neural networks
Physics simulations, Trajectory forecasting, Equivariance, Neural fields, Interacting dynamical systems, Graph neural networks
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
