A Decade of Generative Adversarial Networks for Porous Material Reconstruction

Abstract

Digital reconstruction of porous materials has become increasingly critical for applications ranging from geological reservoir characterization to tissue engineering and electrochemical device design. While traditional methods such as micro-computed tomography and statistical reconstruction approaches have established foundations in this field, the emergence of deep learning techniques, particularly Generative Adversarial Networks (GANs), has revolutionized porous media reconstruction capabilities. This review systematically analyzes 96 peer-reviewed articles published from 2017 to early 2026, examining the evolution and applications of GAN-based approaches for porous material image reconstruction. We categorize GAN architectures into six distinct classes, namely Vanilla GANs, Multi-Scale GANs, Conditional GANs, Attention-Enhanced GANs, Style-based GANs, and Hybrid Architecture GANs. Our analysis reveals substantial progress including improvements in porosity accuracy (within 1% of original samples), permeability prediction (up to 79% reduction in mean relative errors), and achievable reconstruction volumes (from initial $64^3$ to current $2{,}200^3$ voxels). Despite these advances, persistent challenges remain in computational efficiency, memory constraints for large-scale reconstruction, and maintaining structural continuity in 2D-to-3D transformations. This systematic analysis provides a comprehensive framework for selecting appropriate GAN architectures based on specific application requirements.

Figures (40)

• Figure 1: Word cloud visualization generated from titles and abstracts of reviewed publications in GAN-based porous media reconstruction. Font size corresponds to term frequency, revealing key research themes and methodological approaches.
• Figure 2: Overview of GAN architectures for porous media reconstruction. The classification encompasses six distinct architectural paradigms: (1) Vanilla GAN establishing foundational capabilities for 3D reconstruction and multi-phase generation, (2) Multi-Scale GAN enabling hierarchical feature capture and large volume generation through progressive or concurrent training, (3) Conditional GAN providing precise property-controlled generation and manufacturing process optimization, (4) Attention-Enhanced GAN preserving long-range dependencies and structural connectivity through selective feature weighting, (5) Style-based GAN offering hierarchical control from coarse-to-fine features with high-fidelity generation, and (6) Hybrid Architecture GAN combining multiple paradigms to address training stability, data scarcity, and architectural limitations.
• Figure 3: Generative adversarial nets training process. Blue dashed line represents the discriminative distribution (D), black dotted line shows the data generating distribution, and green solid line indicates the generative distribution (G). The four stages illustrate progression from (a) initial partially-trained networks through (b,c) alternating discriminator and generator updates to (d) Nash equilibrium where $p_g = p_{data}$. (Figure adapted from Goodfellow et al. Goodfellow2014GenerativeNetworks)
• Figure 4: Examples of samples generated by GAN model. (a) MNIST digit samples generated by GAN, with the yellow-highlighted column showing the closest training example to demonstrate the model isn't simply memorizing the training data. (b) Face samples generated from the Toronto Face Database (TFD), with the yellow-highlighted column again showing the nearest training examples. These examples demonstrate the GAN's ability to generate realistic synthetic data across different domains. (Figure adapted from Goodfellow et al. Goodfellow2014GenerativeNetworks)
• Figure 5: Schematic representation of the GAN architecture for 3D porous media reconstruction. The generator network (left) transforms random noise z into synthetic 3D structures through sequential transpose convolution layers, while the discriminator network (right) evaluates whether samples are real or generated through volumetric convolution layers. (Figure adapted from Mosser et al. Mosser2017ReconstructionNetworks)