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Local Latent Representation based on Geometric Convolution for Particle Data Feature Exploration
[article]
2022
arXiv
pre-print
Feature related particle data analysis plays an important role in many scientific applications such as fluid simulations, cosmology simulations and molecular dynamics. Compared to conventional methods that use hand-crafted feature descriptors, some recent studies focus on transforming the data into a new latent space, where features are easier to be identified, compared and extracted. However, it is challenging to transform particle data into latent representations, since the convolution neural
arXiv:2105.13240v2
fatcat:crmyeiinurd57h2ku7cu46f6ki