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.本数据集选取格子玻尔兹曼方法（Lattice Boltzmann Method，LBM）模拟生成的顶盖驱动方腔流数据集的四类关键参数，其中X被定义为流向位置，Y被定义为横向位置，U为沿X方向（即流向）的宏观速度，V为沿Y方向（即横向）的宏观速度。本任务中，以X、Y作为输入变量，U、V为机器学习算法的预测目标变量。