Researchers from the RIKEN Center in Japan, in collaboration with The University of Tokyo and Universitat de Barcelona, have made a groundbreaking Milky Way simulation. This model tracks over 100 billion individual stars and spans 10,000 years of galactic evolution. By using artificial intelligence (AI) and innovative simulation techniques, they created a model that is 100 times more detailed and generated it over 100 times faster than previous efforts.
This project, unveiled at the SC ’25 supercomputing conference, is a significant leap for astrophysics and high-performance computing. Applying this approach to climate and weather studies could also be a game changer.
Why Hasn’t This Been Done Before?
Creating detailed Milky Way simulations is no easy task. Astrophysicists have struggled to capture the behavior of individual stars accurately. This complexity arises from the need to calculate gravity, fluid dynamics, chemical reactions, and events like supernovae over vast time scales. Previous models could only simulate systems roughly equivalent to a billion suns, falling short of the Milky Way’s over 100 billion stars. Usually, in earlier models, a single “particle” represented a cluster of around 100 stars, which oversimplified critical processes and reduced overall accuracy.
Simulating stellar evolution accurately requires dividing time into very small increments, making it computationally intensive. For instance, running a simulation for just 1 million years of galactic evolution could take up to 315 hours. To project 1 billion years, that would mean waiting over 36 years—an impractical expectation for researchers.
A New Method with AI
To tackle this issue, Hirashima’s team blended deep learning techniques with traditional simulations. The AI model learned from high-resolution supernova simulations and could predict gas dynamics for up to 100,000 years after a supernova, without straining resources. This allowed researchers to simulate the galaxy’s big-picture behavior while accounting for the finer details of small-scale events, effectively balancing speed with accuracy.
This new method achieves high-resolution simulations of galaxies with over 100 billion stars swiftly. Instead of taking decades, simulating 1 million years now takes around just 2.78 hours, allowing researchers to cover a billion years in only about 115 days.
Broader Implications
This hybrid approach has potential far beyond astrophysics. Fields such as meteorology and oceanography face similar challenges of linking small-scale details with larger patterns. As Hirashima points out, combining AI with high-performance computing could revolutionize how scientists address complex problems.
“This achievement highlights how AI can evolve from merely recognizing patterns to being a core tool in scientific exploration,” Hirashima states.
As we look ahead, it’s clear that AI’s role in research will keep expanding. This could enhance our understanding of not just the Milky Way but also the Earth’s climate and environmental systems. Investing in AI-driven modeling signifies a shift toward more dynamic and precise scientific research.
For further information on advancements in astrophysics and AI, consider checking sources like the American Physical Society or the NASA Astrophysics Division.
Source link
Galaxies; Space Exploration; Astrophysics; Space Telescopes; Computer Modeling; Computers and Internet; Artificial Intelligence; Information Technology
