The computational cost and memory demand required by computational fluid dynamics (CFD) codes simulations can become very high. Therefore, the application of convolutional neural networks (CNN) in this field has been studied owing to its capacity to learn patterns from sets of input data, which can considerably approximate the results of the CFD simulations with relative low errors. DeepCFD code has been taken as a basis and with some slight variations in the parameters of the CNN, while the net is able to solve the Navier–Stokes equations for steady turbulent flows with variable input velocities to the domain. In order to acquire extensive input data to the CNN, a data augmentation technique, which considers the similarity principle for fluid dynamics, is implemented. As a consequence, DeepCFD is able to learn the velocities and pressure fields quite accurately, speeding up the time-consuming CFD simulations.

中文翻译：

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基于数据增强的深度学习技术应用于 CFD 模拟

计算流体动力学 (CFD) 代码模拟所需的计算成本和内存需求可能会变得非常高。因此，已经研究了卷积神经网络 (CNN) 在该领域的应用，因为它具有从输入数据集中学习模式的能力，可以在相对较低的误差下相当接近 CFD 模拟的结果。DeepCFD 代码已作为基础，CNN 的参数略有变化，而网络能够求解 Navier-Stokes 方程，用于具有可变输入速度到域的稳定湍流。为了获取 CNN 的大量输入数据，实施了一种数据增强技术，该技术考虑了流体动力学的相似性原理。作为结果，