mlsanityData to 14 Oct 2026

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

arXiv 2507.01057 ↗First posted 28 Jun 2025cs.LG physics.flu-dyncited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 28 Jun 2025.

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References · 9

  1. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
  2. 2013Network In Networkcited by 605
  3. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  4. 2018Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowscited by 150
  5. 2020Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flowscited by 61
  6. 2021Reinforcement Learning for Adaptive Mesh Refinementcited by 39
  7. 2023Swarm Reinforcement Learning For Adaptive Mesh Refinementcited by 17
  8. 2022An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networkscited by 4
  9. 2022MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamicscited by 1

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