用代理模型加速恒星级星系模拟，算力降75%仍保精度
ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback
- 结合数值模拟与机器学习、吉布斯采样构建代理模型
- 计算成本降低约75%，星形成历史和外流速率匹配真实模拟
- 适合需要高分辨率且资源受限的星系演化研究者
我们提出一种新型高分辨率星系模拟框架，通过代理模型将计算成本降低约75%。质量超过约10 $\mathrm{M_\odot}$ 的大质量恒星会以核心坍缩超新星（CCSNe）形式爆炸，其释放的能量在星系形成中起关键作用，对恒星形成调控及星际介质（ISM）反馈过程至关重要。然而，超新星反馈所需的短积分步长在多尺度天体物理模拟中构成显著瓶颈。为突破此限制，本研究融合直接数值模拟与代理建模技术，包括机器学习和吉布斯采样。模拟得到的星形成历史及星系外流率演化与高分辨率直接模拟结果一致。该方法在保持高分辨率保真度的同时大幅降低计算开销，有效弥合物理尺度差距，支持多尺度模拟。
原文摘要 · Abstract (English)
We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Main Sequence mass of more than about 10 $\mathrm{M_\odot}$ explode as core-collapse supernovae (CCSNe), which play a critical role in galaxy formation. The energy released by CCSNe is essential for regulating star formation and driving feedback processes in the interstellar medium (ISM). However, the short integration timesteps required for SNe feedback have presented significant bottlenecks in astrophysical simulations across various scales. Overcoming this challenge is crucial for enabling star-by-star galaxy simulations, which aim to capture the dynamics of individual stars and the inhomogeneous shell's expansion within the turbulent ISM. To address this, our new framework combines direct numerical simulations and surrogate modeling, including machine learning and Gibbs sampling. The star formation history and the time evolution of outflow rates in the galaxy match those obtained from resolved direct numerical simulations. Our new approach achieves high-resolution fidelity while reducing computational costs, effectively bridging the physical scale gap and enabling multi-scale simulations.
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