Overview
- Hirashima’s RIKEN-led team, with partners at The University of Tokyo and Universitat de Barcelona, modeled the Milky Way star by star over 10,000 years.
- The hybrid method couples a deep-learning surrogate trained on high‑resolution supernova runs to predict ~100,000 years of gas evolution with conventional numerical solvers.
- Performance tests show 1 million years simulated in 2.78 hours, implying ~115 days for 1 billion years versus ~36 years using prior approaches.
- Outputs were validated against large-scale runs on RIKEN’s Fugaku and the University of Tokyo’s Miyabi systems, with deployments reported on up to 7 million CPU cores.
- The work was presented at SC ’25, and the team says the approach could accelerate multi-scale models in climate, weather, and ocean science.