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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Electrostatic field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields

Authors: Kofinas, Miltiadis; Bekkers, Erik Johannes; Nagaraja, Naveen Shankar; Gavves, Efstratios;

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

Abstract

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.

Country
Netherlands
Related Organizations
Keywords

Physics simulations, Trajectory forecasting, Equivariance, Neural fields, Interacting dynamical systems, Graph neural networks

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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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