Artificial Intelligence Enables Full-Scale Simulation of the Milky Way's 100 Billion Stars

A groundbreaking international research collaboration has achieved a significant milestone by simulating the entire Milky Way galaxy, including its estimated 100 billion stars, over a period of 10,000 years. This comprehensive simulation was made possible by the integration of advanced artificial intelligence (AI) algorithms and represents a major leap forward in computational astrophysics.

Historically, galaxy simulations have been constrained by computational limitations, allowing only a fraction of the Milky Way's stars to be modeled at once. Previous efforts could simulate up to one billion stars, treating large groups of stars as single computational units, which restricted the accuracy and detail of the simulations. The new approach, led by the Japanese research institute RIKEN, leverages AI to overcome these challenges, enabling the modeling of each individual star within the galaxy.

This innovative simulation method is not only more detailed but also significantly faster than its predecessors. According to the research team, what previously would have taken several decades to compute can now be accomplished in just a matter of months. For example, simulating one million years of the Milky Way's evolution with the newly developed technique would require only 115 days, compared to an estimated 36 years using older methods.

The breakthrough comes from training AI models with high-resolution data on stellar explosions, particularly supernovae. These models can accurately predict how gases expelled during such explosions disperse through the galaxy over 100,000 years. By automating and optimizing this aspect of the simulation, the researchers could allocate more computational resources to other dynamic processes within the galaxy, resulting in a much more comprehensive simulation.

To ensure the accuracy of their results, the scientists cross-validated the simulation data with outputs from established supercomputer models. The successful verification affirms the reliability of the AI-driven approach and opens up new possibilities for large-scale simulations in various scientific fields.

Beyond advancing astrophysics, the team highlights the broader implications of their methodology. The same AI-accelerated simulation techniques could be adapted to other computationally intensive scientific domains, such as climate modeling, oceanography, and meteorology, where the integration of localized phenomena with system-wide dynamics is essential. This development demonstrates how AI can serve as a powerful tool for accelerating scientific discovery across disciplines.

The enhanced simulation capabilities also provide valuable insights into the origins of elements within the Milky Way. By observing how matter is distributed and transformed in the aftermath of supernovae and other stellar events, researchers can better understand the formation of the chemical building blocks necessary for life on Earth.

The findings of this research were presented at the Supercomputing Conference SC 25 and are publicly accessible for further study. This achievement marks a notable advancement in the use of AI for scientific research, paving the way for more accurate and efficient modeling of complex natural systems.