Physics-Informed Machine Learning for Modeling Turbulence in Supernovae

Abstract

Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) 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 (CNN) to preserve the realizability condition of Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic (MHD) 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.

For our ML algorithm on GitHub, see https://github.com/pikarpov-LANL/Sapsan/wiki/Estimators\#physics-informed-cnn-for-turbulence-modeling

References in corpus (3)

• A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
• Semi-global simulations of the magneto-rotational instability in core collapse supernovae
• MHD Turbulence

Cited by in corpus (6)

• Applications of machine learning in gravitational wave research with current interferometric detectors
• Magnetohydrodynamics with Physics Informed Neural Operators
• Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models
• Turbulence modelling in neutron star merger simulations
• Solving the Pulsar Equation using Physics-Informed Neural Networks
• Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport