Recent progress in augmenting turbulence models with physics-informed machine learning
Recent progress in augmenting turbulence models with physics-informed machine learning
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DOI：
10.1007/s42241-019-0089-y
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发表时间：
2019-12
影响因子：
2.5
通讯作者：
Xinlei Zhang;Jin-Long Wu;O. Coutier-Delgosha;Heng Xiao
中科院分区：
文献类型：
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作者：
Xinlei Zhang;Jin-Long Wu;O. Coutier-Delgosha;Heng Xiao
鉴于传统湍流模型的长期停滞，研究人员尝试使用机器学习来增强湍流模型。本文介绍了我们小组在用物理信息机器学习增强湍流模型方面的一些最新进展。我们还讨论了我们在基于集合的场反演方面的工作，为构建机器学习模型提供训练数据。未来和正在进行的研究工作进行了介绍。
In view of the long stagnation in traditional turbulence modeling, researchers have attempted using machine learning to augment turbulence models. This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning. We also discuss our works on ensemble-based field inversion to provide training data for constructing machine learning models. Future and on-going research efforts are introduced.