AI 中文总结
针对车辆和航空航天工程等领域流体动力学模拟成本高的问题，提出多尺度特征增强图神经网络（ME-GNN），通过两步消息传递、集成注意力U-Net等方法，在三个基准数据集上取得了先进的流体动力学预测结果。
AI 中文摘要
车辆和航空航天工程等领域的工业设计常依赖大规模数值模拟评估流体动力学性能，成本高昂。深度神经网络，尤其是图神经网络（GNNs），有望提高模拟效率，但处理复杂几何形状和大规模网格任务时面临挑战。本文提出多尺度特征增强图神经网络（ME-GNN）应对这些挑战。它采用具有两步消息传递机制的图神经网络有效捕捉详细局部特征，集成注意力U-Net与均匀网格离散化提取粗细特征，利用K-hop采样构建子图促进大数据集高效训练。在三个基准数据集上评估，取得了如ShapeNet-Car速度场相对L2误差0.0196、表面压力0.0556等的先进结果。
英文摘要
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data. However, GNNs face challenges when dealing with tasks involving complex geometries and large-scale meshes. In this paper, we propose the Multi-scale Feature Enhanced Graph Neural Network (ME-GNN) to tackle these challenges. ME-GNN employs a graph neural network with a two-step message-passing mechanism to capture detailed local features effectively. Additionally, it integrates an Attention U-Net with uniform grid discretization, enabling the extraction of both fine and coarse features. The model also utilizes K-hop sampling to construct subgraphs, facilitating efficient training on large datasets while preserving detailed local features. We evaluated ME-GNN on three benchmark datasets and achieved state-of-the-art results: a relative L2 error of 0.0196 for the velocity field and 0.0556 for the surface pressure on ShapeNet-Car, a normalized mean squared error of 0.0033 for the flow field on AirfRANS, and a relative L2 error of 0.1416 for the surface pressure on DrivAerNet.
Comments14 pages
