Physics-informed Machine Learning for Modeling Turbulence in Supernovae
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 01, 2022
- Source ID
- 10.3847/1538-4357/ac88cc
Entities
People
- Chengkun Huang
- Chris Fryer
- Ghanshyam Pilania
- Iskandar Sitdikov
- Platon Karpov
- S. E. Woosley
Organizations
- United States Department of Energy