## 1 Introduction
[15; 16] accurately predicted the wind pressure and pressure coefficient on a cylindrical surface by comparing multiple machine learning methods. [PERSON] et al. [17] utilized convolutional neural networks (CNN) to predict velocity fields for several geometries, with an accuracy rate of about 98%. [PERSON] et al. [18] proposed the use of a Euler distance field to express the aerodynamic shape to predict the lift and drag coefficients and innovatively generated the section shape in the form of pictures, which greatly reduced section shape expression time. [PERSON] et al. [19] used convolutional neural networks (CNNs) to establish a model of the relationship between the pressure field and the velocity field of the cylindrical structure and predicted the wake velocity field based on the surface pressure distribution on the bluff body. [PERSON] and [PERSON] [20] introduced the flow field continuity condition into flow field prediction and analyzed the ability of multi-scale convolutional neural networks and adversarial neural networks to maintain mass conservation and momentum conservation in turbulence prediction. In summary, scholars have begun to explore the successful application of deep learning to related fields of wind engineering, which also provides the basis for this study. When designing the neural network architecture, this paper focuses on the input design and output design of the network. In terms of the input, since simply using the shape as the input of the neural network as a parameter cannot express any complex cross-sections, it is not universal. Therefore, this paper draws on [PERSON]'s idea of using the Euler distance to describe the aerodynamic shape and proposes to describe the shape as a combination of the wall distance field and the space coordinate field, which can distinguish the flow field characteristics at different positions in space and efficiently express the influencingfactors related to aerostatic performance. In terms of the output, the logic of directly predicting the three-component force coefficient from the aerodynamic shape is too jumpy to be conducive to the training and testing of the neural network. Therefore, this paper proposes to divide the aerostatic performance prediction into two steps. First, the pressure field is predicted, and then, the aerostatic three-component force coefficient is obtained by traditional integral calculation. A fully convolutional network, with some improvements, is selected as the neural network. The optimization of the fully convolutional neural network suitable for classification problems is extended to the pixel regression problem, retaining the advantages of the efficient spatial feature extraction of the fully convolutional network, the high-dimensional mapping relationship from shape to pressure field, and the aerostatic performance prediction of bluff body cross-sectional shapes. In addition, considering that CFD numerical simulation can provide a large amount of flow field information as the input and output of a deep learning model, this paper uses CFD simulation to obtain the required data samples for model training and testing. [PERSON] et al. [21] built a DeepCFD model to predict \(U_{x},~{}U_{y}\), and \(p\) by inputting the signed distance function (SDF) and flow region channel, and the prediction effect was excellent. This has some similarities with the research content of this paper. However, this paper pays more attention to the aerostatic performance of the bluff body section. Therefore, a weighted value is added to the wall distance field in the design input to ensure the prediction accuracy of the wall pressure. The space coordinate field is proposed to highlight the characteristics of the flow field. When designing the model, a weighted value is also used for the L2 loss function, and the loss value closer to the aerodynamic shape adopts a weighted value closer to 1. Based on the above overall concept, the second and third chapters of this paper introduce the ideas and implementation details of input and output design.