Neural Multigrid Memory For Computational Fluid Dynamics

TL;DR

This paper introduces MGxTransformer, a novel neural multigrid approach combining VPTR and multigrid architectures to improve turbulent flow simulation accuracy and efficiency across diverse conditions.

Contribution

The paper presents a new neural multigrid method that integrates Video Prediction Transformer with multiscale architectures for enhanced turbulent flow prediction.

Findings

Superior accuracy over baseline models

Effective multiscale turbulence capture

Maintains computational efficiency

Abstract

Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction. In this paper, we propose an advanced data-driven method for simulating turbulent flow, representing a significant improvement over existing approaches. Our methodology combines the strengths of Video Prediction Transformer (VPTR) (Ye & Bilodeau, 2022) and Multigrid Architecture (MgConv, MgResnet) (Ke et al., 2017). VPTR excels in capturing complex spatiotemporal dependencies and handling large input data, making it a promising choice for turbulent flow prediction. Meanwhile, Multigrid Architecture utilizes multiple grids with different resolutions to capture the multiscale nature of turbulent flows, resulting in more accurate and efficient simulations. Through our experiments, we demonstrate the effectiveness of our proposed approach, named MGxTransformer, in accurately predicting velocity, temperature, and turbulence intensity for incompressible turbulent flows across various geometries and flow conditions. Our results exhibit superior accuracy compared to other baselines, while maintaining computational efficiency. Our implementation in PyTorch is available publicly at https://github.com/Combi2k2/MG-Turbulent-Flow