论文标题
物理信息的机器学习，用于建模超新星的湍流
Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
论文作者
论文摘要
湍流在天体物理现象中起着重要作用，包括核心偏离超新星（CCSN），但是由于直接数值模拟（DNS）过于昂贵，因此当前的模拟必须依靠亚网格模型。不幸的是，现有的子网格模型不够准确。最近，机器学习（ML）表现出令人印象深刻的预测能力来计算湍流闭合。我们已经开发了一个具有物理信息的卷积神经网络（CNN），以保留雷诺应力的可靠性条件，这对于准确的湍流压力预测是必需的。此处测试了ML亚网格模型的适用性，以在固定和动态状态下磁性水力动力学（MHD）湍流。我们未来的目标是利用CCSN框架中的这种ML方法（在GitHub上获得）来研究准确模块的湍流对这些恒星爆炸的影响。
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.
