Scientists Unveil AI-Enhanced Milky Way Model Transforming Astronomy

A groundbreaking simulation leveraging artificial intelligence has provided scientists with an unprecedented view of the Milky Way’s evolution. This new model tracks over **100 billion individual stars** over a span of **10,000 years**, achieving a level of detail previously unattainable in astrophysics. Traditionally, simulations grouped stars into larger clusters, obscuring the finer-scale physics that dictate the growth and transformation of galaxies. The latest method marks a significant departure from these earlier techniques, paving the way for more accurate astrophysical research.

AI-Powered Insights into Galactic Evolution

The project, spearheaded by researcher **Keiya Hirashima** at the **RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS)** in Japan, involved collaboration with experts from the **University of Tokyo** and the **University of Barcelona**. The team recently presented their findings at **SC’25**, an international conference focusing on high-performance computing and analysis.

To tackle the complexities of galactic modeling, Hirashima’s team integrated a deep learning surrogate model. This AI was trained on high-resolution simulations of supernova behavior, enabling it to predict gas dispersion in the aftermath of an explosion. This innovation allows the primary simulation to advance significantly faster while maintaining the intricate details of individual supernova events. The approach was validated using data from Japan’s **Fugaku supercomputer** and the **Miyabi system** at the University of Tokyo.

Efficiency and Future Applications

The result is a comprehensive Milky Way simulation that achieves true individual-star resolution with remarkable efficiency. The simulation can now model **one million years** of galactic evolution in just **2.78 hours**, meaning a full **billion years** of history can be simulated in approximately **115 days** instead of the previous estimate of **36 years**.

Hirashima emphasized the broader implications of their work, stating, “This achievement shows that AI-accelerated simulations can move beyond pattern recognition to become a genuine tool for scientific discovery.” The research indicates that similar methodologies could enhance simulations across various fields, including cosmic structure formation, black hole accretion, and even Earth system modeling.

As the team looks to the future, they aim to further scale their technique and explore its potential applications in environmental science. This milestone not only contributes to our understanding of the Milky Way but also represents a pivotal shift in how computational sciences can harness AI to address complex multi-physics problems.