流动完备化网络：利用图神经网络从不完全流动信息推断流体动力学
Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks
摘要：
本文介绍了一种新的神经网络--流补网络（flow completion network，FCN），它可以从基于图卷积注意力网络（Graph Convolution Attention Network）的不完全数据中推断出流体动力学，包括流场和物体受力。FCN由几个图卷积层和空间注意层组成。该方法与涡力图（VFM）方法相结合，可以得到流场的速度场和涡力贡献。与流体动力学中采用的其他神经网络相比，该方法既能处理结构化数据，又能处理非结构化数据。建议的FCN的性能进行评估的计算流体动力学（CFD）数据上的流场周围的圆柱。我们的模型预测的力系数与直接从CFD获得的力系数进行了验证。此外，我们的模型有效地利用了现有的流场信息和梯度信息的同时，给出了一个更好的性能比传统的卷积神经网络（CNN）为基础的模型和深度神经网络（DNN）为基础。具体而言，在不同雷诺数和不同比例的训练数据集的所有情况下，结果表明，提出的FCN在测试数据集上实现了最大范数均方误差为5.86%，远低于传统的基于CNN和基于DNN的模型（分别为42.32%和15.63%）。
英文摘要：
This paper introduces a novel neural network - flow completion network (FCN) - to infer the fluid dynamics, includ-ing the flow field and the force acting on the body, from the incomplete data based on Graph Convolution AttentionNetwork. The FCN is composed of several graph convolution layers and spatial attention layers. It is designed to inferthe velocity field and the vortex force contribution of the flow field when combined with the vortex force map (VFM)method. Compared with other neural networks adopted in fluid dynamics, the FCN is capable of dealing with bothstructured data and unstructured data. The performance of the proposed FCN is assessed by the computational fluiddynamics (CFD) data on the flow field around a circular cylinder. The force coefficients predicted by our model arevalidated against those obtained directly from CFD. Moreover, it is shown that our model effectively utilizes the exist-ing flow field information and the gradient information simultaneously, giving a better performance than the traditionalconvolution neural network (CNN)-based and deep neural network (DNN)-based models. Specifically, among all thecases of different Reynolds numbers and different proportions of the training dataset, the results show that the proposedFCN achieves a maximum norm mean square error of 5.86% in the test dataset, which is much lower than those of thetraditional CNN-based and DNN-based models (42.32% and 15.63% respectively).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
