Imagine holding the entire Milky Way in the palm of your hand, every star, every cosmic dance, simulated with breathtaking precision. That’s exactly what researchers at Japan’s RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) have achieved—a feat so groundbreaking it’s rewriting the rules of astrophysics. But here’s where it gets controversial: Can a computer simulation truly capture the complexity of our galaxy, or are we missing something fundamentally human in the process? Let’s dive in.
In collaboration with the University of Tokyo and the Universitat de Barcelona, the team has created the world’s first hyper-realistic simulation of the Milky Way, modeling over 100 billion stars across 10,000 years. What’s jaw-dropping is not just the scale—100 times more stars than previous models—but the speed. This simulation was produced 100 times faster than ever before. How? By harnessing the power of 7 million CPU cores, cutting-edge machine learning algorithms, and advanced numerical simulations. The result? A model that’s a game-changer for astrophysics, supercomputing, and AI development.
Published in the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC ’25), the research paper titled ‘The First Star-by-star N-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model’ (https://dl.acm.org/doi/10.1145/3712285.3759866) reveals how this simulation allows astronomers to study stellar and galactic evolution on an unprecedented scale. And this is the part most people miss: It’s not just about stars; it’s about testing theories of galactic formation, structure, and evolution by comparing simulations to real astronomical observations.
For decades, scientists have struggled to simulate the Milky Way with such detail. Why? Because galaxies are mind-bogglingly complex systems. Gravity, fluid dynamics, supernovae, element synthesis, and supermassive black holes all play roles—each on vastly different scales. Traditional supercomputers hit a wall here. Even state-of-the-art systems would take over 36 years to simulate just 1 billion years of galactic evolution, a tiny fraction of the Milky Way’s 13.61 billion-year lifespan. Adding more cores doesn’t solve the problem; it just guzzles more energy and reduces efficiency.
Enter the AI shortcut. Led by Hirashima, the team developed a machine learning surrogate model that operates independently of the main simulation. Trained on high-resolution supernova data, this AI predicts how explosions shape the surrounding gas and dust over 100,000 years. When combined with physical simulations, it enables the modeling of both large-scale galactic dynamics and small-scale stellar phenomena simultaneously. As Hirashima puts it, ‘This achievement shows that AI-accelerated simulations can move beyond pattern recognition to become a genuine tool for scientific discovery.’
But the real test came with large-scale trials on the Fugaku and Miyabi Supercomputer Systems. The results? Stunning. The model simulated 1 million years of galactic evolution in just 2.78 hours—a task that would’ve taken 315 hours with traditional methods. Extrapolate that, and 1 billion years of galactic history could be simulated in just 115 days. Bold claim alert: This isn’t just a win for astrophysics; it’s a blueprint for revolutionizing complex simulations in meteorology, ocean dynamics, and climate science.
Yet, the question lingers: Are we truly capturing the essence of the cosmos, or are we simplifying it beyond recognition? As we marvel at this technological triumph, let’s also ponder the limits of our tools. What do you think? Is this simulation a leap forward or a step into uncharted territory? Share your thoughts below—let’s spark a cosmic debate!
