FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this limitation, but t...

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Bibliographic Details
Published in:Materials 2023-06, Vol.16 (13), p.4740
Main Author: Argilaga, Albert
Format: Article
Language:English
Subjects:
Coefficients
Control theory
CT imaging
Data augmentation
Deep learning
Generative adversarial networks
Image analysis
Liquors
Machine learning
Mathematical models
Mechanical properties
Molecular weight
Nonparametric statistics
Partial differential equations
Permeability
Physics
Polymers
Pore size
Porosity
Porous materials
Reproducibility
Shear strength
Supervision
X ray imagery
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Summary:X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this limitation, but they often lack a sound physical basis. This work presents a physics-supervised generative adversarial networks (GANs) model and applies it to the generation of X-ray μCT images. FEM simulations provide physical information in the form of elastic coefficients. Negative X-ray μCT images of a Hostun sand were used as the target material. During training, image batches were evaluated with nonparametric statistics to provide posterior metrics. A variety of loss functions and FEM evaluation frequencies were tested in a parametric study. The results show, that in several test scenarios, FEM-GANs-generated images proved to be better than the reference images for most of the elasticity coefficients. Although the model failed at perfectly reproducing the three out-of-axis coefficients in most cases, the model showed a net improvement with respect to the GANs reference. The generated images can be used in data augmentation, the calibration of image analysis tools, filling incomplete X-ray μCT images, and generating microscale variability in multiscale applications.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma16134740