{"type":"ScholarlyArticle","title":"A Survey of Machine Learning-Based Physics Event Generation","authors":[{"name":"Yasir Alanazi","slug":"yasir-alanazi","given":"Yasir","family":"Alanazi","affiliation":""},{"name":"Nobuo Sato","slug":"nobuo-sato","given":"Nobuo","family":"Sato","affiliation":""},{"name":"Pawel Ambrozewicz","slug":"pawel-ambrozewicz","given":"Pawel","family":"Ambrozewicz","affiliation":""},{"name":"Astrid Hiller-Blin","slug":"astrid-hiller-blin","given":"Astrid","family":"Hiller-Blin","affiliation":""},{"name":"Wally Melnitchouk","slug":"wally-melnitchouk","given":"Wally","family":"Melnitchouk","affiliation":""},{"name":"Marco Battaglieri","slug":"marco-battaglieri","given":"Marco","family":"Battaglieri","affiliation":""},{"name":"Tianbo Liu","slug":"tianbo-liu","given":"Tianbo","family":"Liu","affiliation":""},{"name":"Yaohang Li","slug":"yaohang-li","given":"Yaohang","family":"Li","affiliation":""}],"abstract":"Event 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.","doi":"10.24963/ijcai.2021/588","url":"https://ijcai.org/proceedings/2021/588","pdf":"https://ijcai.org/proceedings/2021/0588.pdf","markdown":"https://ijcai.org/proceedings/2021/588.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/588","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":588,"pages":{"first":4286,"last":4293},"keywords":["Machine learning: General","Multidisciplinary topics and applications: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}