TY  - EJOU
AU  - Jiang, Weixin 
AU  - Li, Zongze 
AU  - Yuan, Qing 
AU  - Gong, Junhua 
AU  - Yu, Bo 

TI  - High-Resolution Flow Field Reconstruction Based on Graph-Embedding Neural Network
T2  - The International Conference on Computational \& Experimental Engineering and Sciences

PY  - 2024
VL  - 30
IS  - 1
SN  - 1933-2815

AB  - High resolution flow field results are of great significance for exploring physical laws and guiding practical engineering practice. However, traditional activities based on experiments or direct numerical solutions to obtain high-resolution flow fields typically require a significant amount of computational time or resources. In response to this challenge, this study proposes an efficient and robust high-resolution flow field reconstruction method by embedding graph theory into neural networks, to adapt to low data volume situations. In the high resolution flow field reconstruction problem of an NS equation, the proposed model has a lower mean squared error on the test set compared that of physics-informed neural network and classical neural network models. Among them, the low resolution data comes from 0.1% high resolution data. And the predicted results of the proposed model at time 10s is also closest to the direct numerical simulation results. The proposed model exhibits great advantages in robustness.
KW  - Flow field reconstruction; graph embedding; neural network; high resolution prediction

DO  - 10.32604/icces.2024.011266