论文标题
评估监督的机器学习方法的流体流量
Assessment of supervised machine learning methods for fluid flows
论文作者
论文摘要
我们将监督的机器学习技术应用于流体动力学的许多回归问题。根据规范流问题的特征，准确性，计算成本和鲁棒性，对四个机器学习体系结构进行了检查。我们考虑从表面上有限数量的传感器估算力系数和唤醒，以便在圆柱体上流动和带有gurney瓣的NACA0012机翼。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唤醒，二维衰减各向同性湍流和三维湍流流动的超分辨率分析中使用卷积神经网络的使用。在总结的评论中，我们总结了此处考虑的一系列回归类型问题的发现。
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.
