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<p>Out of an astronomical number of combinations in the materials design space, the quest for high-performance novel composites is pacing up. Composite designing needs predictive advanced tools and techniques to complement advanced manufacturing techniques and avoid costly experimental trials, complex modeling, and simulations. Current work focuses on developing an unsupervised material science-informed deep learning model architecture, Graph-GAN, for end-to-end prediction of deformation fields from the unseen composite designs accurately without any spatial data loss. The rate-independent J2 plasticity model for small deformations (5%) is modified for computing deformation fields in synthetically generated biphasic 2D microstructures in a python-based platform for ground-truth dataset generation. Graph-GAN performed impeccably well over typical traditional generative adversarial networks (image-to-image translations) with RMSE &lt; 0.06 for prediction in the test dataset with regular circular geometries and RMSE &lt; 0.07 for unseen microstructures with irregular and arbitrary geometries for the secondary phase. For the first time, this work presents the integration of graph convolutional networks (GCNs) into generative adversarial networks (GANs) for predictive materials science using machine learning. This approach paves the way for numerous opportunities for experts across various disciplines to explore similar methodologies.</p> Graphical abstract <p></p>

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Unsupervised Graph-GAN model for stress–strain field prediction in a composite

  • 期刊论文

  • 发表日期：

    2025年3月23日

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  • Ratanlal Sahu,
  • Antrakrate Gupta,
  • Divyansh Mittal,
  • Parthapratim Chatterjee,
  • Shikhar Krishn Jha

摘要

Out of an astronomical number of combinations in the materials design space, the quest for high-performance novel composites is pacing up. Composite designing needs predictive advanced tools and techniques to complement advanced manufacturing techniques and avoid costly experimental trials, complex modeling, and simulations. Current work focuses on developing an unsupervised material science-informed deep learning model architecture, Graph-GAN, for end-to-end prediction of deformation fields from the unseen composite designs accurately without any spatial data loss. The rate-independent J2 plasticity model for small deformations (5%) is modified for computing deformation fields in synthetically generated biphasic 2D microstructures in a python-based platform for ground-truth dataset generation. Graph-GAN performed impeccably well over typical traditional generative adversarial networks (image-to-image translations) with RMSE < 0.06 for prediction in the test dataset with regular circular geometries and RMSE < 0.07 for unseen microstructures with irregular and arbitrary geometries for the secondary phase. For the first time, this work presents the integration of graph convolutional networks (GCNs) into generative adversarial networks (GANs) for predictive materials science using machine learning. This approach paves the way for numerous opportunities for experts across various disciplines to explore similar methodologies.

Graphical abstract