In a groundbreaking scientific advancement, researchers from the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) in Japan, in conjunction with collaborators from The University of Tokyo and the Universitat de Barcelona in Spain, have achieved an unprecedented simulation of the Milky Way galaxy. This simulation uniquely models more than 100 billion individual stars over a timespan of 10,000 years, harnessing the power of artificial intelligence coupled with state-of-the-art numerical simulations. This monumental accomplishment surpasses previous models by an order of magnitude in both the scale of stars represented and the speed of simulation, setting a new benchmark in computational astrophysics and multi-scale scientific modeling.
Astrophysics has long sought to produce a detailed, star-by-star simulation of the Milky Way, essential for testing prevailing theories about the galaxy’s formation, structural dynamics, and the life cycles of stars within it. The methodological complexities, however, are immense. Galaxy evolution modeling must simultaneously account for interactions governed by gravity, fluid dynamics within interstellar gas, the energetic outputs of supernova explosions, and the intricate processes of element synthesis spanning drastically different scales of space and time. This intrinsic multi-physics, multi-scale nature imposes formidable computational demands that have, until now, limited simulation fidelity.
Conventional simulations historically capped at representing galaxies with an aggregate mass roughly equivalent to a billion suns. Given that the Milky Way comprises over 100 billion stars, each particle in such models typically symbolizes a cluster of about 100 suns, which blurs the minutiae of individual stellar events. This granularity gap means that smaller-scale phenomena, particularly those evolving rapidly such as supernova explosions, remain under-resolved since their dynamics unfold on timescales and spatial scales far finer than what the timestep resolution allows. The crux of this undersampling lies in the trade-off between timestep granularity and computational feasibility—a fine timestep is essential to capturing fast, small-scale processes but substantially amplifies the computational cost.
Attempting to remedy these limits by merely increasing the computational cores is inefficient and unsustainable. Not only does scaling hardware demand exorbitant energy consumption, but diminishing returns emerge due to decreasing parallel efficiency. As an example, current leading-edge physical simulations would require approximately 315 uninterrupted hours to simulate just one million years of stellar evolution with individual star resolution. Scaling to one billion years at this pace would translate into an investment of over 36 real-time years, rendering such endeavors impractical.
The research team, led by Keiya Hirashima, proposed a novel solution that synergizes deep learning with conventional physical simulations. By training a surrogate deep neural network model on detailed, high-resolution numerical simulations of supernova events, the AI component learned to emulate the expansion of supernova remnant gas across 100,000 years post-explosion. Critically, this surrogate acts as an efficient proxy within the larger galactic simulation, enabling fine-scale phenomena to be accurately captured without the need to repetitively solve computationally intense physical equations for every localized event.
This integration of AI into high-performance computing frameworks allows the simulation to concurrently resolve both the macroscopic galactic dynamics and microscale stellar explosions. Validations conducted on RIKEN’s Fugaku supercomputer and The University of Tokyo’s Miyabi system demonstrated the model’s fidelity in reproducing astrophysical phenomena across scales. The surrogate model’s incorporation slashed the necessary computing time dramatically, with a one million-year galactic evolution now achievable in just 2.78 hours of wall-clock time.
Consequently, projections indicate that this method can simulate one billion years of Milky Way evolution in around 115 days, a quantum leap from the previous decades-long expected runtimes. This accelerated temporal compression fundamentally alters what can be computationally explored in astrophysics, opening pathways to exhaustively investigate star formation histories, spiral arm dynamics, and chemical enrichment processes within our galaxy at unprecedented detail.
The broader implications of this advancement extend into various scientific fields grappling with multi-scale and multi-physics challenges. For example, climate and weather modeling, characterized by complex interactions between global atmospheric circulation and localized convective events, can potentially benefit from AI-augmented surrogate models to bridge scale gaps. Oceanography, ecological modeling, and other domains requiring the coupling of rapid local phenomena with slow global trends may also exploit this methodology for efficient, accurate simulations.
Hirashima underscored the significance of this approach, stating that merging AI with high-performance computing heralds a paradigm shift in addressing computational challenges endemic to the physical sciences. He emphasized that AI-enhanced simulations transcend mere pattern recognition, evolving into powerful scientific instruments capable of revealing intricate causal pathways underlying natural phenomena. This is especially poignant in astrophysics, where tracing the origin and evolution of elements critical to life demands such granular, robust modeling.
This pioneering research thus exemplifies the transformative potential of interdisciplinary strategies, blending computational science, astrophysics, and AI to tackle long-standing scientific puzzles. The successful digital replication of the Milky Way at star-level resolution not only fulfills a decades-old ambition but also sets a precedent for future explorations into the cosmic and earthly systems governed by intertwined scales and physical laws.
For the scientific community, this progress invites a reevaluation of simulation approaches, encouraging the development of similar surrogate-empowered frameworks tailored to other challenging domains. As computational resources continue to expand and AI methodologies advance, the horizon of possible simulations widens, enabling deeper understanding of complex systems that shape our universe and environment.
This achievement marks a milestone in computational astrophysics and demonstrates the promise of artificial intelligence as a tool not just for data analysis but for accelerating fundamental scientific discovery across disciplines. The integration of physical knowledge and AI opens new frontiers for simulating reality with both scale and precision, a breakthrough that resonates far beyond the Milky Way.
Subject of Research: Astrophysics, Computational Simulation, Artificial Intelligence, Milky Way Galaxy Modeling
Article Title: AI-Powered Simulation Achieves Unprecedented Milky Way Galaxy Modeling at Star-Level Resolution
News Publication Date: Not specified
Web References: http://dx.doi.org/10.1145/3712285.3759866
References: Published in the international supercomputing conference SC ’25
Image Credits: RIKEN
Keywords: Space sciences, Astrophysics, Astronomy, Theoretical astrophysics, Applied sciences and engineering, Computer science, Artificial intelligence, Machine learning, Deep learning, Supercomputing, Computer simulation, Galaxy formation, Physical cosmology, Cosmology, Milky Way, Spiral galaxies, Galaxies, Celestial bodies, Supernovae, Stellar physics, Weather simulations, Applied ecology, Ecological modeling, Climate modeling
