The simulation marks a significant leap in both astrophysics and computational science. Traditionally, simulating galaxies at such a detailed level has been nearly impossible due to the vast amount of data and time required. However, the new method developed by the RIKEN team combines advanced machine learning techniques with supercomputing power, allowing them to model the Milky Way’s evolution over 10,000 years in just hours. This innovation opens new possibilities for understanding galactic formation, star evolution, and other large-scale cosmic phenomena.
Overcoming the Billion-Particle Barrier
According to research from the iTHEMS team, existing simulations of galaxies often struggle to break past the “billion-particle barrier.” Until now, most models could simulate only up to a billion solar masses, significantly underestimating the scale of galaxies like the Milky Way, which contains over 100 billion stars. By combining 7 million CPU cores with machine learning, the researchers were able to model 300 billion particles, a leap in particle resolution and scale that has never been achieved before.
This advance comes after years of astrophysical challenges in simulating galaxies with high accuracy. The forces at play in a galaxy, such as gravity, fluid dynamics, and supernova explosions, all occur on vastly different scales, making them difficult to model simultaneously. According to the team, the new method’s ability to model these complex phenomena in one simulation provides scientists with a much more detailed and realistic view of galactic behavior. As a result, astronomers can now use this model to test and refine theories about how galaxies, including our own, evolved over billions of years.
AI Speeds Up Galactic Evolution Simulations
One of the major breakthroughs of this simulation is the use of a machine learning model, or “surrogate model,” to handle small-scale phenomena like supernova explosions. Normally, such events require short timesteps, significantly slowing down simulations. However, by training the AI on data from high-resolution supernova simulations, the team was able to predict the behavior of surrounding gas without using the same resources as the rest of the model. This AI-driven approach accelerated the simulation, enabling the researchers to model 1 million years of galactic evolution in just 2.78 hours.
This development is important not only for astrophysics but for other fields requiring large-scale simulations. According to the researchers, this new AI-assisted method could be applied to fields such as climate science, where simulating the effects of global weather systems and environmental changes involves similar challenges of balancing small- and large-scale phenomena. The success of this AI approach in astrophysics opens doors for further research in diverse scientific areas, where accuracy and speed are crucial.
Breaking Through Supercomputing Limits
The team’s use of supercomputing power is another key aspect of the simulation’s success. By running their model on two of the world’s most advanced supercomputers, Fugaku and Miyabi, the team was able to push the boundaries of computational capability. According to the RIKEN team, simulating a galaxy with over 100 billion stars would normally take 36 years using conventional supercomputers. However, thanks to the AI shortcut and innovative methods, the new simulation can complete the same task in 115 days.
The ability to simulate such complex systems in such a short time is a monumental achievement in supercomputing. It not only accelerates the study of galactic evolution but also sets a new benchmark for large-scale simulations. By integrating AI with high-performance computing, the team has created a tool that will likely transform how scientists approach simulations in many different domains.