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arXiv 2011.14820math.NAcs.LGcs.NA

利用空间填充曲线将卷积神经网络应用于非结构化网格数据

Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves

Imperial College London(帝国理工学院);Department of Chemical Engineering, Imperial College London(帝国理工学院化学工程系);Data Science Institute, Imperial College London(帝国理工学院数据科学研究所)

Claire E. Heaney, Yuling Li, Omar K. Matar, Christopher C. Pain

AI总结:

本文提出首个可直接处理非结构化网格数据的经典CNN,以空间填充曲线将多维数据转为一维表示,通过卷积自编码器实现,经测试其准确性可与结构化网格上的经典CAE相比,还可用于圆柱绕流等实际问题。

摘要:

本文提出首个可直接应用于非结构化有限元网格或控制体积网格数据的经典卷积神经网络(CNN)。CNN在图像分类和图像压缩领域极具影响力,这两个领域通常处理结构化网格上的数据。非结构化网格常用于求解偏微分方程,尤其适用于需要网格贴合复杂几何形状或需要可变网格分辨率的问题。该方法的核心是空间填充曲线,其遍历网格的节点或单元,形成一条尽可能短(就边数而言)且恰好访问每个节点或单元一次的路径。空间填充曲线(SFCs)用于找到节点或单元的排序方式,将非结构化网格上的多维解转换为一维(1D)表示,随后可对其应用一维卷积层。尽管该方法是在二维中开发的,但也适用于更高维的问题。为演示该方法,我们选择的网络是卷积自编码器(CAE),不过也可使用其他类型的CNN。通过将CAE应用于经SFC重新排序的数据集来测试该方法。自编码器的输入和输出使用稀疏层,并探索了多个SFC的使用。我们比较了基于SFC的CAE与应用于结构化网格上两个理想化问题的经典CAE的准确性,然后将该方法应用于使用有限元法和非结构化网格获得的圆柱绕流解。

英文摘要:

This paper presents the first classical Convolutional Neural Network (CNN) that can be applied directly to data from unstructured finite element meshes or control volume grids. CNNs have been hugely influential in the areas of image classification and image compression, both of which typically deal with data on structured grids. Unstructured meshes are frequently used to solve partial differential equations and are particularly suitable for problems that require the mesh to conform to complex geometries or for problems that require variable mesh resolution. Central to the approach are space-filling curves, which traverse the nodes or cells of a mesh tracing out a path that is as short as possible (in terms of numbers of edges) and that visits each node or cell exactly once. The space-filling curves (SFCs) are used to find an ordering of the nodes or cells that can transform multi-dimensional solutions on unstructured meshes into a one-dimensional (1D) representation, to which 1D convolutional layers can then be applied. Although developed in two dimensions, the approach is applicable to higher dimensional problems. To demonstrate the approach, the network we choose is a convolutional autoencoder (CAE) although other types of CNN could be used. The approach is tested by applying CAEs to data sets that have been reordered with an SFC. Sparse layers are used at the input and output of the autoencoder, and the use of multiple SFCs is explored. We compare the accuracy of the SFC-based CAE with that of a classical CAE applied to two idealised problems on structured meshes, and then apply the approach to solutions of flow past a cylinder obtained using the finite-element method and an unstructured mesh.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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