Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

Mueller, Juliane; Ju, Xiangyang; Dumont, Vincent

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DOE OSTI · 2438306

Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

Mueller, Juliane·Ju, Xiangyang·Dumont, Vincent

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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.

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high energy physics hyperparameter tuning machine learning MATHEMATICS AND COMPUTING

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Mueller, Juliane, Ju, Xiangyang, Dumont, Vincent. 2024-08-21. Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations. https://www.osti.gov/biblio/2438306

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