Can I read SuperVortexNet: Reconstructing Superfluid Vortex Filaments Using Deep Learning on EtoBox?

SuperVortexNet: Reconstructing Superfluid Vortex Filaments Using Deep Learning by Keepfer, Nick; Flynn, Thomas; Parker, Nick; Billam, Thomas is a scholarly article available to read on EtoBox.

What is SuperVortexNet: Reconstructing Superfluid Vortex Filaments Using Deep Learning about?

We introduce a novel approach to the three-dimensional reconstruction of superfluid vortex filaments using deep convolutional neural networks. Superfluid vortices, quantum mechanical phenomena of immense scientific interest, are challenging to image due to their small dimensions and intricate topology. Here, we propose a deep-learning methodology that serves as a proof-of-principle for fully reconstructing the topology of superfluid vortex filaments. We have trained a convolutional neural network on a large dataset of simulated superfluid density images obtained by solving the Gross--Pitaevskii equation at scale, enabling it to learn the complex patterns and features inherent to superfluid vortex filaments. The network ingests the integrated density along the axial, coronal, and sagittal directions and outputs the reconstructed superfluid vortex filaments in three dimensions. We demonstrate the success of this approach over a range of vortex densities of simulated isotropic quantum turbulence, enabling access to the characteristic scaling law of the decaying vortex line length.

Author

Keepfer, Nick; Flynn, Thomas; Parker, Nick; Billam, Thomas

Published

2023

Language

EN