Surrogate modeling for Supernova feedback
Supernovae (SNe) are the explosions at the end of the life of massive stars. They are one of the most important sources of energy and momentum, which drive the evolution of galaxies. However, simulating the feedback from SNe is computationally expensive because it requires resolving the multiscale physical processes with tiny timesteps. In response, we have developed a surrogate model using machine learning to duplicate supernova feedback quickly. When a supernova explodes in a galaxy simulation, the model duplicates the SN feedback 100,000 years ahead on-the-fly. This approach accelerates the simulation by a factor of 100 compared to the direct simulation. (Hirashima et al. 2023a,b)
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