超临界翼型预测与逆向设计的网格无关解码器
Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design
AI总结：
本文提出一种结合数据驱动快速预测与网格无关灵活性的隐式解码器，用于超临界翼型快速预测，并联合网格编码器构建生成模型实现逆向设计。
摘要：
网格无关模型在处理非结构化空间数据和融入偏微分方程方面具有优势。近年来，它们被广泛研究用于构建物理信息神经网络，但需要逐案例训练且训练时间较长。另一方面，气动外形设计需要快速预测和设计工具，而数据驱动的基于网格的模型已取得良好性能。因此，本文提出了一种数据驱动的网格无关解码器，结合了数据驱动模型的快速预测能力和网格无关模型的灵活性。该模型被称为隐式解码器，由两个子网络组成，即ShapeNet和HyperNet。ShapeNet基于隐式神经表示，HyperNet是一个简单的神经网络。隐式解码器被训练用于超临界翼型的快速预测。比较了不同的激活函数，并提出了空间约束以提高模型的可解释性和泛化能力。然后，隐式解码器与基于网格的编码器一起用于构建生成模型，该模型用于具有指定物理特征的超临界翼型的逆向设计。
英文摘要：
Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features.
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。
