Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
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DOI：
10.1063/5.0040286
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发表时间：
2020-12
影响因子：
4.6
通讯作者：
Adam Subel;A. Chattopadhyay;Yifei Guan;P. Hassanzadeh
中科院分区：
文献类型：
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作者：
Adam Subel;A. Chattopadhyay;Yifei Guan;P. Hassanzadeh
发展数据驱动的亚网格尺度大涡模拟（LES）模式已受到广泛关注。尽管取得了一些成功，特别是在先验（离线）测试中，但已经确定了挑战，包括后验（在线）测试和泛化（即，外推）训练的数据驱动的SGS模型，例如更高的雷诺数。在这里，使用随机强迫Burgers湍流作为测试平台，我们证明了使用适当预处理（增强）数据训练的深度神经网络可以产生稳定和准确的后验LES模型。此外，我们还证明了迁移学习可以准确/稳定地推广到雷诺数高10倍的流动。
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.