Advancements in Galaxy Simulation with AI and Supercomputing

In a significant step towards enhancing our understanding of the universe, a team of scientists has made substantial progress in galaxy simulation using a blend of artificial intelligence and supercomputing. This innovation could revolutionize how we study star formation and galaxy evolution, paving the way for broader applications in fields like climate and meteorology.

Challenges in Simulating Every Star in Our Galaxy

Building accurate models of the Milky Way, which allow for tracking each star individually, has long been a goal for astronomers. These models help compare theories about galaxy evolution and star formation with observed data. However, achieving this requires complex calculations involving gravity, fluid dynamics, chemical element formation, and supernova activity over vast time and space scales, making the task extremely complex.

Current galaxy simulation capabilities remain limited, with models representing systems roughly equivalent to a billion solar masses, far less than the over 100 billion stars in the Milky Way. This means that the smallest “particle” in these models represents a group of about 100 stars, reducing the accuracy of small-scale processes.

The Challenge of Simulated Time and Computing Requirements

To detail rapid events like supernova evolution, simulations must progress in extremely small time steps, significantly increasing computational effort. Even with the best current physical models, simulating the Milky Way star by star requires about 315 hours for each million years of galaxy evolution, meaning a billion-year simulation would take over 36 years of real-time.

Increasing the number of processors in supercomputers is not a practical solution, as energy consumption rises and efficiency decreases with more processors.

A New Approach Using Deep Learning

To overcome these obstacles, the Hiroshima team developed a method that combines an alternative model based on deep learning with traditional physical simulations. The alternative model was trained using high-resolution supernova simulations, learning to predict how gas spreads over 100,000 years post-explosion without needing additional resources from the main simulation.

This intelligent component enabled researchers to capture the overall behavior of the galaxy while modeling small-scale events in detail, including the intricate details of each supernova explosion. This approach was validated by comparing its results with large-scale runs on supercomputers like Fugaku and Miyabi.

Broader Potential in Climate and Weather Fields

This hybrid approach could reshape many areas of computational science that require linking small-scale physics to large-scale behavior. Fields such as meteorology, ocean sciences, and climate change face similar challenges and could benefit from tools that accelerate complex multidimensional simulations.

Hiroshima states that integrating artificial intelligence with supercomputing represents a fundamental shift in how multidimensional, multiphysics problems are addressed across computational sciences.

Conclusion

This achievement marks a qualitative leap in astronomical simulation, allowing us a deeper understanding of how our galaxy and its stars evolve. It also opens doors to broader applications in other fields requiring complex, multidimensional simulations. By using artificial intelligence, these simulations can go beyond pattern recognition to become a true tool for new scientific discoveries.