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We apply generative adversarial network (GAN) technology to build an eventgenerator that simulates particle production in electron-proton scattering thatis free of theoretical assumptions about underlying particle dynamics. Thedifficulty of efficiently training a GAN event simulator lies in learning thecomplicated patterns of the distributions of the particles physical properties.We develop a GAN that selects a set of transformed features from particlemomenta that can be generated easily by the generator, and uses these toproduce a set of augmented features that improve the sensitivity of thediscriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is ableto faithfully reproduce the distribution of final state electron momenta ininclusive electron scattering, without the need for input derived fromdomain-based theoretical assumptions. The developed technology can play asignificant role in boosting the science of existing and future acceleratorfacilities, such as the Electron-Ion Collider.

Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)