We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems. We consider the estimation of force coefficients and wakes from a limited number of sensors on the surface for flows over a cylinder and NACA0012 airfoil with a Gurney flap. The influence of the temporal density of the training data is also examined. Furthermore, we consider the use of convolutional neural network in the context of super-resolution analysis of two-dimensional cylinder wake, two-dimensional decaying isotropic turbulence, and three-dimensional turbulent channel flow. In the concluding remarks, we summarize on findings from a range of regression-type problems considered herein.

中文翻译：

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流体流动的监督机器学习方法的评估

我们将监督机器学习技术应用于流体动力学中的许多回归问题。从特性、准确性、计算成本和规范流问题的鲁棒性方面检查了四种机器学习架构。我们考虑了对流过圆柱体和带有格尼襟翼的 NACA0012 翼型的流动的表面上有限数量传感器的力系数和尾流的估计。还检查了训练数据的时间密度的影响。此外，我们考虑在二维圆柱尾流、二维衰减各向同性湍流和三维湍流通道流的超分辨率分析背景下使用卷积神经网络。在结束语中，我们总结了本文考虑的一系列回归类型问题的发现。