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

arXiv: https://arxiv.org/abs/2410.23346 · HTML: https://mlsanity.com/paper/2410.23346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [Learning to Predict the Cosmological Structure Formation](https://mlsanity.com/paper/1811.06533/) (2018)
- [Field Level Neural Network Emulator for Cosmological N-body Simulations](https://mlsanity.com/paper/2206.04594/) (2022)
- [3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations](https://mlsanity.com/paper/2302.00026/) (2023)
- [Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning](https://mlsanity.com/paper/2210.02339/) (2022)
- [GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity](https://mlsanity.com/paper/2308.08010/) (2023)

## Built on by (most cited first, 2 of 2)

- [The First Star-by-star N-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model](https://mlsanity.com/paper/2510.23330/) (2025)
- [From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics](https://mlsanity.com/paper/2512.01576/) (2025)

## Abstract (arXiv)

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 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.