2021/06/04 by Ari Frankel, Cosmin Safta, Frankel, Ari +6
Computer Science · Engineering · #Advanced Mathematical Modeling in Engineering #Enhanced Oil Recovery Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #cs.LG
paper · pdf · doi:10.48550/arxiv.2107.00090
45 pages, 19 figures
openalex publication_date 2021/06/04 · arxiv created 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.