A Decade of Generative Adversarial Networks for Porous Material Reconstruction

TL;DR

This review comprehensively analyzes the evolution and application of GANs in porous material reconstruction over a decade, highlighting advancements, challenges, and categorization of architectures for diverse scientific and engineering uses.

Contribution

It systematically categorizes GAN architectures for porous media reconstruction and summarizes progress, challenges, and application-specific recommendations from 96 peer-reviewed articles.

Findings

Porosity accuracy within 1% of original samples

Permeability prediction errors reduced by up to 79%

Reconstruction volume increased from 64^3 to 2200^3 voxels

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.