@misc{1dba833ecfd940c29eef8fee26e4d10f,
title = "Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations",
abstract = "We present our SciDAC FASTMath-HEP partnership results for tuning generative adversarial models (GANs) for high energy physics applications. The GANs are used in hybrid simulations to accelerate otherwise time-consuming computations. We optimize for both, prediction accuracy and variability with the goal to find GAN architectures that are reliable and robust.",
keywords = "high energy physics, hyperparameter tuning, machine learning",
author = "Juliane Mueller and Xiangyang Ju and Vincent Dumont",
year = "2023",
language = "American English",
series = "Presented at the SciDAC-5 Principal Investigator (PI) Meeting, 12-14 September 2023, Rockville, Maryland",
publisher = "National Laboratory of the Rockies (NLR)",
address = "United States",
type = "Other",
}