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Title: ExaGAN v1.0

Abstract

This software provides a basic Generative Adversarial Network (GAN) template for modeling 2D slices of N-body dark matter simulations, as part of the Exalearn project. The underlying design is a standard deep convolutional GAN, but we augment it with a technique we are calling multi-channel rescaling. This technique adds a second image channel to the output of the GAN, which represents the data in a different normalization in order to improve the quality of the results.

Developers:
 [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Release Date:
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
USDOE

Primary Award/Contract Number:
AC02-05CH11231
Code ID:
27755
Site Accession Number:
2019-129
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Country of Origin:
United States

Citation Formats

Harrington, Peter, and USDOE. ExaGAN v1.0. Computer software. https://www.osti.gov//servlets/purl/1569222. USDOE. 12 Jul. 2019. Web. doi:10.11578/dc.20191003.3.
Harrington, Peter, & USDOE. (2019, July 12). ExaGAN v1.0 [Computer software]. https://www.osti.gov//servlets/purl/1569222. doi:10.11578/dc.20191003.3.
Harrington, Peter, and USDOE. ExaGAN v1.0. Computer software. July 12, 2019. https://www.osti.gov//servlets/purl/1569222. doi:10.11578/dc.20191003.3.
@misc{osti_1569222,
title = {ExaGAN v1.0},
author = {Harrington, Peter and USDOE},
abstractNote = {This software provides a basic Generative Adversarial Network (GAN) template for modeling 2D slices of N-body dark matter simulations, as part of the Exalearn project. The underlying design is a standard deep convolutional GAN, but we augment it with a technique we are calling multi-channel rescaling. This technique adds a second image channel to the output of the GAN, which represents the data in a different normalization in order to improve the quality of the results.},
url = {https://www.osti.gov//servlets/purl/1569222},
doi = {10.11578/dc.20191003.3},
year = {2019},
month = {7},
note =
}

Software:
Publicly Accessible Repository
https://github.com/pzharrington/ExaGAN

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