Data Prediction in Two-Dimensional Cavity Flows in a Square Domain

four important parameters of lid driven cavity data from LBM simulations are considered as X tends to stream wise position, Y tends to Transverse position, U tends to Macroscopic velocity along X i.e., streamwise direction, and V tends to Macroscopic velocity along Y i.e., transverse direction. X and Y will be the input variables, U and V are the target variables for prediction based on Machine learning algorithm.

本数据集选取基于格子玻尔兹曼方法（Lattice Boltzmann Method，LBM）模拟得到的顶盖驱动方腔流（lid driven cavity）的四类关键参数：其中X对应流向位置，Y对应横向位置，U代表沿X方向（即流向）的宏观速度，V代表沿Y方向（即横向）的宏观速度。以X、Y作为输入变量，U、V为基于机器学习算法开展预测任务的目标变量。