Physics-informed Machine Learning for Modeling Turbulence in Supernovae

Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.

Automated summary

The article discusses the importance of turbulence in astrophysical phenomena and the challenges in simulating it. The authors have developed a physics-informed convolutional neural network using machine learning to predict turbulent pressure accurately. The study tests the applicability of this method in different turbulent conditions and aims to use it in core-collapse supernova simulations in the future.