We define and study a fully-convolutional neural network stochastic model, NN-Turb, which generates 1-dimensional fields with turbulent velocity statistics. Thus, the generated process satisfies the Kolmogorov 2/3 law for second order structure function. It also presents negative skewness across scales (i.e. Kolmogorov 4/5 law) and exhibits intermittency. Furthermore, our model is never in contact with turbulent data and only needs the desired statistical behavior of the structure functions across scales for training.

翻译：我们定义并研究了一种全卷积神经网络随机模型NN-Turb，它可以生成具有湍流速度统计特征的一维多元场。因此，生成的过程满足Kolmogorov 2/3定律对于二阶结构函数。在各个尺度下，它还表现出负偏度（即Kolmogorov 4/5定律）和间歇现象。此外，我们的模型从未接触过湍流数据，只需要在训练时提供具有所需尺度下结构函数统计行为的数据即可。