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Journal of Machine Learning for Modeling and Computing

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MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE

卷 3, 册 1, 2022, pp. 1-30
DOI: 10.1615/JMachLearnModelComput.2021039688
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Ari L. Frankel
Sandia National Laboratories, Livermore, California 94551, USA
Cosmin Safta
P.O.Box 969, MS 9051; Computational Science and Analysis, Sandia National Laboratories, Livermore, CA, 94551
Coleman Alleman
Sandia National Laboratories, Livermore, California 94551, USA
Reese E. Jones
Sandia National Laboratories, Livermore, California 94551, USA

摘要

Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. Compared to feature-based and pixel-based convolutional neural network models, the proposed method has a number of advantages: (a) it is deep in that it does not require featurization but can benefit from it, (b) it has a simple implementation with standard convolutional filters and layers, (c) it works natively on unstructured and structured grid data without interpolation (unlike pixel-based convolutional neural networks), and (d) it preserves rotational invariance like other graph-based convolutional neural networks. We demonstrate the performance of the proposed network and compare it to traditional pixel-based convolution neural network models and feature-based graph convolutional neural networks on multiple large datasets.

键词: graph neural network, material microstructure, homogenization
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