Para2Mesh: A dual diffusion framework for moving mesh adaptation
Year: 2025
Type: article
Abstract: Multi-scale problems in Computational Fluid Dynamics (CFD) often require numerous simulations across various design parameters. Using a fixed mesh for all cases may fail to capture critical physical features. Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes. However, most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications. In addition, generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships. The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations. A dual diffusion framework, Para2Mesh, is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model. Through iterative denoising, the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information, and then achieve rapid and reliable mesh movement. Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method. It could become a real-time adaptation tool to assist engineering design and optimization, providing a promising solution for high-resolution flow-field analysis.
Source: Chinese Journal of Aeronautics
Institution: Nanjing University of Aeronautics and Astronautics
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,000
Awards: G6485807040, G7916290996
OpenAlex ID: W4408020105