Scientists used 7 million CPU cores to simulate 100 billion stars in the most comprehensive Milky Way simulation to date.
An international team of researchers has performed a mind-bending simulation of the Milky Way .For the first time, the team successfully performed a simulation of over 100 billion individual stars, showcasing their evolution over the course of 10 thousands years.
The scientists leveraged AI and numerical simulations for their state-of-the-art simulation. According to the team, this allowed them to represent 100 times more individual stars than the next-best model. They were also able to do so a hundred times faster.Simulating the Milky WayTo date, scientists have not been able to model a large number of individual stars while simulating large galaxies like the Milky Way as a whole.The new simulation takes into account element synthesis, supernova explosions, gravity, and fluid dynamics. Each of these takes place over vastly different time scales, making them incredibly challenging to simulate simultaneously.The team of international researchers, led by Keiya Hirashima at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences in Japan, included scientists from the University of Tokyo and Universitat de Barcelona in Spain. They believe their new methodology can be applied to other astrophysical studies and even climate science. Before the team’s new method, the best existing simulations had an upper mass limit of roughly a billion suns. The Milky Way is home to more than 100 billion stars, meaning this would not give an accurate representation. Only large-scale events could be accurately represented, as the smallest “particle” in the simulation would represent a cluster of stars with a mass of 100 suns.The team, who published their findings in the international supercomputing conference SC ’25, verified their results by comparing them to the output of large-scale tests from RIKEN’s supercomputer Fugaku and the University of Tokyo’s Miyabi Supercomputer System.The new simulation could help astrophysicists test theories on the formation of stars, galaxies, and other cosmic processes that help to better understand our place in the universe.Simulating a billion years in 115 daysAccording to the scientists behind the new study, even the best conventional simulations would take a prohibitively long time to achieve their results. Simulating the Milky Way down to the individual star would require 315 hours for every million years of simulation time, for example. At that rate, it would take over 36 years to simulate a billion years of galaxy evolution.As adding more and more supercomputer cores is not a viable option, the team developed a new approach that combines a deep learning surrogate model with physical simulations. The deep learning model was trained on high-resolution simulations of a supernova, allowing it to learn how gas expands in the 100,000 years after a supernova explosion.This allowed for individual star resolution in large galaxies with over 100 billion stars. Using this method, the simulation time for 1 million years only took 2.78 hours. This means a billion years could be simulated in only 115 days.“I believe that integrating AI with high-performance computing marks a fundamental shift in how we tackle multi-scale, multi-physics problems across the computational sciences,” Hirashima explained in a press statement. “This achievement also shows that AI-accelerated simulations can move beyond pattern recognition to become a genuine tool for scientific discovery—helping us trace how the elements that formed life itself emerged within our galaxy.”
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