Scientists have achieved computational milestones by using artificial intelligence to significantly speed up the Galaxy’s development simulations, reducing processing time by about 75% while maintaining scientific accuracy.
AI-driven approaches allow researchers to model supernova explosions and galaxy formation in months rather than years, potentially unleashing new insights into how our own galaxy develops and creates elements essential to life.
This represents the first successful application of machine learning in accelerating galaxy simulations, which opens up possibilities for studying larger cosmic systems.
Computing Challenge
Milky Way simulations face a fundamental bottleneck: they must capture events that occur over very different time frames. While typical interstellar processes take place for millions of years, crucial supernova dynamics occur only over a few hundred years, creating a 1,000-fold difference in the required time resolution.
“When we use AI models, the simulations are four times faster than standard numerical simulations,” explains Keiya Hirashima of the Center for Interdisciplinary Theory and Mathematical Sciences of Riken. “This corresponds to a reduction in calculation time from several months to half a year.”
It takes 1-2 years for a traditional supercomputer to simulate a relatively small dwarf galaxy with appropriate resolution. A new framework called Asura-FDPS-ML addresses this challenge by replacing the most expensive computational calculations with AI predictions.
Training AI in a stellar explosion
The team trained their neural network using detailed simulations of a single supernova in 300 molecular clouds, each of which contains 1 million solar materials. AI has learned to predict how gas density, temperature and speed develop 100,000 years after a supernova explosion.
Key technical achievements include:
- Calculation cost reduction for galaxy simulations by 75%
- The accurate reappearance of star formation history and galaxy outflow
- Save complex multiphase gas structures in simulated galaxies
- Successfully model thermal supersonic and cool subsonic flow
Mixed calculation method
Instead of replacing all calculations, the system uses a hybrid approach. AI processes supernova explosions in dense areas, traditional methods have encountered difficulties in extremely small time periods, while direct numerical simulations continue to be used in fewer areas of computational requirements.
Key technical details of the complete study: The researchers fixed the simulation time period at 2,000 years, when AI predictions were used, compared to variable time periods (sometimes only a few hundred years), and traditional methods required to use during supernova modeling.
The AI model runs in complex frameworks using multiple processor groups. When supernova occurs in dense areas, the affected areas are sent to the dedicated AI processors that predict the results, while the main simulation continues to run the wider Galaxy development.
Scientific verification
“It is crucial that our AI-assisted simulations are able to reproduce the important dynamics used to capture galaxy evolution and material cycles, including star formation and galaxy outflow,” Hirashima noted.
Verification proof is comprehensive. AI accelerated simulations match traditional results of the complex physics of the Milky Way morphology, star formation rate and galactic winds. The model successfully captures how hot gases move energy away from the galaxy, while cooler gases transport most of the mass, a key difference in understanding galaxy evolution.
Interestingly, the AI approach reveals some differences in the environmental conditions of the supernova, suggesting that it may actually handle dense area explosions more accurately than traditional thermal injection methods.
The meaning of the universe
Advances are expected to viablely alter astrophysical research by making previously impossible simulations. Current Galaxy simulations often model dwarf systems with limited resolution, but new methods can study galaxies of the size of the Milky Way at a single star level in detail.
According to Hirashima, “Our AI-assisted framework will allow high-resolution star simulations of heavy galaxies, such as the Milky Way, with the aim of predicting the origin of the solar system and elements that are crucial to the birth of life.”
The team has applied their framework to galaxy-scale simulations, with the potential for new insights into how the helical structures of our galaxy are formed and how supernovae are distributed to distribute heavy elements of planets (and life).
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