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：我们定义并研究一个完全革命性神经网络随机模型NN-Turb,该模型产生一维字段,具有动荡速度统计数据。因此,生成过程满足了用于第二秩序结构功能的科尔莫戈罗夫2/3法律,还呈现出跨尺度(即科尔莫戈罗夫4/5法律)的负面偏差和展示间隙。此外,我们的模型从未接触过动荡的数据,只需要各尺度结构功能所需的统计行为来进行培训。