World's First Milky Way Simulation Models 100 Billion Stars With Record-Breaking Speed
Groundbreaking Milky Way Simulation Achieved
Researchers have achieved the world's first Milky Way simulation capable of accurately modelling more than 100 billion individual stars across a span of 10,000 years. This breakthrough, driven by a fusion of artificial intelligence and numerical techniques, delivers a model 100 times more detailed than previous efforts—and completes the task over 100 times faster.
The study, published in the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, demonstrates a major breakthrough at the nexus of astrophysics, supercomputing, and AI. Its methodology may also be harnessed to model broader systems such as climate trends and meteorological shifts.
Challenges of Simulating the Milky Way
Astrophysicists have long sought to simulate the Milky Way at the level of individual stars, enabling them to test theories of galactic formation, structure and stellar evolution against real-world data. Crafting such precise models is notoriously challenging, as they must incorporate gravity, fluid behaviour, supernova events and chemical enrichment—each unfolding across vastly different spatial and temporal scales.
Until recently, researchers have been unable to capture a galaxy the size of the Milky Way while preserving fine star-by-star detail. Existing simulations cap out at around one billion solar masses, far short of the Milky Way's more than 100 billion stars.
Consequently, each modelled "particle" effectively represents a cluster of roughly 100 suns, smoothing out the behaviour of individual stars and limiting accuracy to broader galactic processes.
The core difficulty lies in the timescale between simulation steps. Rapid stellar-level changes, such as supernova evolution, can only be resolved if snapshots of the galaxy are taken frequently enough to capture them.
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Computational Limits and the Need for Innovation
However, processing finer timesteps demands significantly more computing power. Even setting aside today's mass-resolution limits, the most advanced traditional simulation would require 315 hours to recreate just one million years of the Milky Way's take over 36 years in real time. Simply adding more supercomputer cores is not a practical answer—they consume vast energy, and additional cores do not guarantee proportional speed gains.
To overcome this barrier, Keiya Hirashima of RIKEN's iTHEMS in Japan, together with collaborators from the university of Tokyo and the Universital de Barcelona, devised a new strategy that integrates a deep-learning surrogate model with physical simulations.
The surrogate model was trained on high-resolution simulations of a supernova, enabling it to predict the expansion of surrounding gas over 100,000 years after an explosion without drawing on the rest of the simulation's resources. This AI-driven shortcut allowed the system to capture both the galaxy's broad-scale behaviour and detailed events such as supernovae. To assess its accuracy, the researchers compared the results with large-scale tests run on RIKEN's Fugaku supercomputer and the University of Tokyo's Miyabi system.
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Breakthrough Results and Broader Implications
The technique not only delivers individual-star resolution for vast galaxies containing more than 100 billion stars, but also reduces the time needed to simulate one million years to just 2.78 hours. At this pace, a full billion-years evolution could be completed in merely 115 days rather than 36 years.
Beyond astronomy, the method has the potential to revolutionize other multi-scale simulations—in meteorology, oceanography and climate science—where links between fine and large-scale processes are essential.
"Integrating AI with high-performance computing represents a profound shift in how we approach multi-scale, multi-physics challenges across computational science." Hirashima notes.
"This breakthrough further demonstrates that AI-driven simulations can progress beyond pattern-matching to serve as a true instrument of discovery, helping reveal how the elements that built life itself were forged in our galaxy."
Why This Matters
- First simulation to model 100+ billion stars individually
- 100x more detail, 100x faster than previous methods
- Breakthrough powered by AI +numerical physics
- Potential applications in climate science, weather modeling, oceanography
Who Developed It
- Keiyan Hirashima (RIKEN iTHEMS, Japan)
- University of Tokyo
- Universitat de Barcelona
Why It's a Game Change
Resolves star-by-star processes including supernova evolution
Reduces billion-year simulation time from 36 years to 115 days
Demonstrates new frontier in AI-driven scientific discovery
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