Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies

Galaxy simulations have found the interdependence of multiscale gas physics, such as star formation, stellar feedback, inflow/outflow, and so on, by improving the physical models and resolution. The mass resolution remains capped at around 1,000 solar masses (e.g., Applebaum et al. 2021). To overcome the limitations, we are developing a new N-body/SPH code, ASURA-FDPS, to leverage exascale computing (e.g., Fugaku), handle approximately one billion particles, and simulate individual stars and stellar feedback within the galaxy. However, the emergence of communication costs hinders scalability beyond one thousand CPU cores. One of the causes is short timescale events localized in tiny regions, such as supernova explosions. In response, we have developed a surrogate model using machine learning to duplicate supernova feedback quickly (Hirashima et al., 2023a,b). In the presentation, I report the fidelity and progress of the simulations with our new machine-learning technique.