一种风向监督双流神经网络OACSTPCD

Aiming at the problem that the single modeling method under the one-dimensional Burgers equation is difficult to fully consider the influence of wind direction on the coefficient at different stages,and cannot effectively obtain the relevant information between nodes.A wind direction supervised two-stream neural network is proposed to predict the finite difference coefficients of up and down wind direc-tions separately.At the same time,a wind direction judgment module is designed to realize the weight fusion of the predicted finite difference coefficients.The two-stream neural network is supervised by the wind direction,combined with the prior knowledge to assign a certain weight to the learned coefficients,so as to highlight the different influences of the upper and lower wind directions on the prediction re-sults,and can effectively realize the prediction of points in different wind directions,making the spatial structure characteristics Information mining is more sufficient,thereby improving the accuracy of differ-ential coefficient prediction.While the grid resolution is 4 to 8 times thicker than the traditional numeri-cal solution method,it improves the accuracy of the work of the Google team,thereby increasing the calculation speed.