Physics-Informed Machine Learning for Modeling Turbulence in Supernovae

Understand

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.