  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.
