Work

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

Year: 2024

Type: article

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.

Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)

Author: Mueller, Juliane; Ju, Xiangyang; Dumont, Vincent

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4402466701

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