论文标题
通过转移学习，使用深度学习对较高的雷诺数字进行深度学习，对强迫汉堡的湍流进行数据驱动的亚网格规模建模
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
论文作者
论文摘要
开发大型涡流模拟（LES）的数据驱动的亚网格尺度（SGS）模型最近受到了极大的关注。尽管有一些成功，尤其是在先验（离线）测试中，但已经确定了在训练有素的数据驱动的SGS模型的后验（在线）测试和概括（即额外化）中包括数值不稳定性的挑战，例如，对较高的Reynolds数字。在这里，使用随机强迫的汉堡湍流作为测试床，我们表明，使用适当预先调节（增强）数据训练的深神经网络可产生稳定且准确的后验les模型。此外，我们表明传递学习可以准确/稳定的概括到雷诺数高10倍的流量。
Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LES) has received substantial attention recently. Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models, for example to higher Reynolds numbers. Here, using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models. Furthermore, we show that transfer learning enables accurate/stable generalization to a flow with 10x higher Reynolds number.
