Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates

Lushun Fan, Yuqin Xia, Jun Li, Karl Jenkins
6/28/2025
cs.LGphysics.flu-dyn

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Abstract

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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Cite this paper

@article{fan2025loopnet,
  title  = {Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates},
  author = {Lushun Fan and Yuqin Xia and Jun Li and Karl Jenkins},
  year   = {2025},
  eprint = {2507.01057},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.01057v1}
}

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