FFANet: Dual Attention-Based Flow Field Aware Network for 3D Grid Classification and Segmentation

Deep learning-based approaches for three-dimensional (3D) grid understanding and processing tasks have been extensively studied in recent years. Despite the great success in various scenarios, the existing approaches fail to effectively utilize the velocity information in the flow field, resulting in the actual requirements of post-processing tasks being difficult to meet by the extracted features. To fully integrate structural information in the 3D grid and velocity information, this paper constructs a flow-field-aware network (FFANet) for 3D grid classification and segmentation tasks. The main innovations include: (i) using the self-attention mechanism to build a multi-scale feature learning network to learn the distribution feature of the velocity field and structure feature of different scales in the 3D flow field grid, respectively, for generating a global feature with more discriminative representation information; (ii) constructing a fine-grained semantic learning network based on a co-attention mechanism to adaptively learn the weight matrix between the above two features to enhance the effective semantic utilization of the global feature; (iii) according to the practical requirements of post-processing in numerical simulation, we designed two downstream tasks: 1) surface grid identification task and 2) feature edge extraction task. The experimental results show that the accuracy (Acc) and intersection-over-union (IoU) performance of the FFANet compared favourably to the 3D mesh data analysis approaches.