ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback

2024-10-30

arXiv.org

AI Summary

In galaxy simulations, core-collapse supernova (CCSN) feedback necessitates prohibitively small time steps, severely limiting computational efficiency—especially in star-by-star resolution simulations spanning multiple physical scales. To address this, we propose a physics-informed surrogate modeling framework that integrates deep learning with Gibbs sampling, enabling the first efficient joint solution of CCSN feedback while preserving high physical fidelity. Our method reduces the computational cost of the feedback module by ~75%, effectively overcoming the traditional time-step bottleneck. When deployed in high-resolution, star-by-star galaxy simulations, it reproduces stellar formation histories and gas outflow temporal evolution indistinguishable from those obtained via full-physics direct simulations. This work bridges the long-standing modeling gap between stellar-scale physics and galactic-scale dynamics, significantly enhancing both the feasibility and computational efficiency of multi-scale, high-fidelity galaxy evolution simulations.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

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.

Problem

• Reducing computational cost of supernova feedback simulations
• Modeling energy regulation in star formation processes
• Bridging scale gaps in multi-scale galaxy simulations

Innovation

• Surrogate model reduces computation cost by 75%
• Combines direct simulations with machine learning
• Enables high-resolution star-by-star galaxy simulations