A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data. However, GNNs face challenges when dealing with tasks involving complex geometries and large-scale meshes. In this paper, we propose the Multi-scale Feature Enhanced Graph Neural Network (ME-
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- PossiblePossibly related (embedding) · 50%JuliaGraphs/GraphNeuralNetworks.jl →
- PossiblePossibly related (embedding) · 47%NVIDIA/physicsnemo →
- PossiblePossibly related (embedding) · 45%Principled approaches for extending neural architectures to function spaces for operator learning →
- LinkedLinked via arxiv author · 85%Li Xiao →
“A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries”
- LinkedLinked via arxiv author · 85%Tianyu Li →
“A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries”
- LinkedLinked via arxiv author · 85%Yiye Zou →
“A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries”
- LinkedLinked via arxiv author · 85%Mingjie Zhang →
“A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries”
- LinkedLinked via arxiv author · 85%Xiaogangd Deng →
“A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries”