mlsanityData to 7 Oct 2026

Mesh-based graph convolutional neural networks for modeling materials with microstructure

arXiv 2107.00090 ↗First posted 4 Jun 2021cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 99 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

27 of 99 references.

References · 27

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15 more
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Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 0; 2024: 3.

7 papers.

  1. 2022Rewiring with Positional Encodings for Graph Neural Networkscited by 35
  2. 2022Modular machine learning-based elastoplasticity: generalization in the context of limited datacited by 16
  3. 2024A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulationscited by 7
  4. 2022A heteroencoder architecture for prediction of failure locations in porous metals using variational inferencecited by 2
  5. 2022Deep learning and multi-level featurization of graph representations of microstructural datacited by 2
  6. 2024Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processescited by 1
  7. 2024Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical responsecited by 0