Mesh-based graph convolutional neural network models of processes with complex initial states

• URL: http://arxiv.org/abs/2107.00090v1
• Date: Fri, 4 Jun 2021 03:40:40 GMT
• Title: Mesh-based graph convolutional neural network models of processes with complex initial states
• Authors: Ari Frankel and Cosmin Safta and Coleman Alleman and Reese Jones
• Abstract summary: We propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. 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 three large datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 three large datasets.

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