UCNN: A Convolutional Strategy on Unstructured Mesh
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Abstract (arXiv)
In machine learning for fluid mechanics, fully-connected neural network (FNN) only uses the local features for modelling, while the convolutional neural network (CNN) cannot be applied to data on structured/unstructured mesh. In order to overcome the limitations of FNN and CNN, the unstructured convolutional neural network (UCNN) is proposed, which aggregates and effectively exploits the features of neighbour nodes through the weight function. Adjoint vector modelling is taken as the task to study the performance of UCNN. The mapping function from flow-field features to adjoint vector is constructed through efficient parallel implementation on GPU. The modelling capability of UCNN is compared with that of FNN on validation set and in aerodynamic shape optimization at test case. The influence of mesh changing on the modelling capability of UCNN is further studied. The results indicate that UCNN is more accurate in modelling process.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 12 Jan 2021.
Builds on
18 of 38 references.
References · 18
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
6 more
- 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
- 2015Diffusion-Convolutional Neural Networkscited by 268
- 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 211
- 2017SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernelscited by 160
- 2016Learning shape correspondence with anisotropic convolutional neural networkscited by 95
- 2018Recent progress in semantic image segmentationcited by 39
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2 papers.