Physics-Informed Machine Learning for Modeling Turbulence in Supernovae

TL;DR

This paper introduces a physics-informed CNN model for turbulence in supernovae, aiming to improve accuracy over existing subgrid models by preserving physical constraints, with promising tests on MHD turbulence.

Contribution

The paper presents a novel physics-informed CNN that maintains realizability conditions for turbulence modeling in astrophysics, enhancing predictive accuracy.

Findings

Successful application to MHD turbulence regimes

Preserves physical realizability conditions

Potential for improved supernova simulations

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.