Researchers in Japan have developed the first simulation of the Milky Way galaxy that tracks more than 100 billion individual stars by combining artificial intelligence with supercomputing capabilities. Presented at the SC ’25 supercomputing conference in St. Louis, the model simulates 10,000 years of galactic evolution and operates 100 times faster than prior techniques to address computational limitations in modeling large-scale cosmic structures.
Prior simulations reached the state of the art by managing galaxies with stellar masses equivalent to about one billion suns, which represents only one-hundredth of the Milky Way’s actual stellar population. These efforts relied on conventional physics-based methods that demand extensive processing power. For instance, such approaches require 315 hours to compute one million years of galactic evolution. Extending this to a billion-year timeframe would necessitate more than 36 years of continuous computation, rendering full-scale Milky Way simulations impractical for most research timelines.
The advancement stems from a deep learning surrogate model trained using high-resolution simulations of supernova events. This artificial intelligence element acquires the ability to forecast the expansion of gas over the 100,000 years after a supernova detonation. By doing so, it eliminates the need for numerous small, resource-intensive timesteps in the overall simulation process while preserving the precision of physical outcomes. The research team deployed this system across 7 million CPU cores, utilizing RIKEN’s Fugaku supercomputer alongside the University of Tokyo’s Miyabi system, to achieve these efficiencies.
With this setup, the simulation time dropped to 2.78 hours for each million years of evolution, allowing a projection spanning one billion years to complete in roughly 115 days. Hirashima stated, “Integrating AI with high-performance computing marks a fundamental shift in how we tackle multi-scale, multi-physics problems across the computational sciences.” He highlighted potential uses in climate modeling, weather prediction, and oceanography, where challenges arise from connecting small-scale phenomena to broader system dynamics.
The resulting simulation permits scientists to follow the emergence of elements vital for life throughout the galaxy’s history. This capability provides insights into the chemical evolution processes that contributed to the formation of planets resembling Earth.
