Alanazi YSato NAmbrozewicz PHiller-Blin AMelnitchouk WBattaglieri MLiu TLi Y.2022-11-152022-11-15202110450823https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125437382&partnerID=40&md5=5ee415512862879c3ea606e022aa8f81https://scholars.lib.ntu.edu.tw/handle/123456789/625079Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific challenges, and discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology. © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.Machine learning; Building physics; Event generators; Generative model; High energy nuclear physics; High-energy particle physics; Machine learning models; Machine-learning; Modeling designs; Nuclear and particle physics; State of the art; Surveysconference paper2-s2.0-85125437382