用神经网络从稀疏机翼坐标生成高质量网格，提升气动仿真效率
Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates
- 基于卷积神经网络，从稀疏边界坐标生成网格
- 通过双损失函数优化，生成网格质量显著提升
- 适合流体仿真、航空设计等领域的工程师快速建模
本研究提出一种基于深度卷积神经网络的智能网格优化系统，实现机翼网格的生成与优化。核心为Loop2Net生成器与双损失函数设计，根据给定的机翼坐标预测网格结构，并在训练过程中通过两个关键损失函数持续优化模型性能。通过引入惩罚项约束，最终实现高质量网格生成目标。该方法显著提升网格生成效率与质量，适用于复杂气动外形的快速仿真建模。
原文摘要 · Abstract (English)
In this study, an innovative intelligent optimization system for mesh quality is proposed, which is based on a deep convolutional neural network architecture, to achieve mesh generation and optimization. The core of the study is the Loop2Net generator and loss function, it predicts the mesh based on the given wing coordinates. And the model's performance is continuously optimised by two key loss functions during the training. Then discipline by adding penalties, the goal of mesh generation was finally reached.
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