  Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating
fluid flow across diverse industries. High-resolution CFD simulations offer
valuable insights into fluid behavior and flow patterns, aiding in optimizing
design features or enhancing system performance. However, as resolution
increases, computational data requirements and time increase proportionately.
This presents a persistent challenge in CFD. Recently, efforts have been
directed towards accurately predicting fine-mesh simulations using coarse-mesh
simulations, with geometry and boundary conditions as input. Drawing
inspiration from models designed for super-resolution, deep learning techniques
like UNets have been applied to address this challenge. However, these existing
methods are limited to structured data and fail if the mesh is unstructured due
to its inability to convolute. Additionally, incorporating geometry/mesh
information in the training process introduces drawbacks such as increased data
requirements, challenges in generalizing to unseen geometries for the same
physical phenomena, and issues with robustness to mesh distortions. To address
these concerns, we propose a novel framework, PointSAGE a mesh-independent
network that leverages the unordered, mesh-less nature of Pointcloud to learn
the complex fluid flow and directly predict fine simulations, completely
neglecting mesh information. Utilizing an adaptable framework, the model
accurately predicts the fine data across diverse point cloud sizes, regardless
of the training dataset's dimension. We have evaluated the effectiveness of
PointSAGE on diverse datasets in different scenarios, demonstrating notable
results and a significant acceleration in computational time in generating fine
simulations compared to standard CFD techniques.
