# PointSAGE: Mesh-independent superresolution approach to fluid flow predictions

arXiv: https://arxiv.org/abs/2404.04615 · HTML: https://mlsanity.com/paper/2404.04615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations](https://mlsanity.com/paper/2010.00072/) (2020)
- [Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations](https://mlsanity.com/paper/2311.09740/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction](https://mlsanity.com/paper/2407.03925/) (2024)

## Abstract (arXiv)

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.