High-resolution computational fluid dynamics (CFD) simulations are essential for capturing complex fluid flow phenomena such as turbulent flows and shock-boundary layer interactions that are critical for aerospace applications. However, the high computational cost of obtaining simulation results at high resolution often limits their practicality. Traditional deep learning-based super-resolution methods such as UNets have been explored to predict fine-mesh simulation results from coarse-mesh simulations, but they face challenges with unstructured meshes and require extensive amounts of training data. To address these limitations, we propose Implicit-PointSAGE, a mesh-independent super-resolution framework that leverages unordered, mesh-less structure of point clouds to simulate intricate fluid dynamics. By integrating the strengths of PointSAGE with a Neural Implicit Learning module employing Galerkin-based attention, the framework efficiently captures the underlying physics and accurately predicts fine-mesh data directly from coarse-mesh inputs. Implicit-PointSAGE demonstrates significant computational efficiency, scalability to diverse point cloud sizes, and adaptability across various CFD scenarios. It matches the performance of state-of-the-art surrogate models in solving problems represented by PDEs, achieving high accuracy with 10 to 100 times fewer data samples-sometimes requiring as few as 100 samples-while delivering substantial computational acceleration. These results position Implicit-PointSAGE as a transformative tool for super-resolution modeling and efficient high-resolution CFD simulations.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号