Generative adversarial networks for reconstruction of three-dimensional porous media from two-dimensional slices

Structure-property relationships in fibrous meniscal tissue through image-based augmentation

This study introduces an adaptive three-dimensional (3D) image synthesis technique for creating variational realizations of fibrous meniscal tissue microstructures. The method allows controlled deviation from original geometries by modifying parameters such as porosity, pore size and specific surface area of image patches. The unbiased reconstructed samples matched the morphological and hydraulic properties of original tissues, with relative errors generally below 10%. Additional samples were generated with predefined deviations to increase dataset diversity. Analysis of 1500 synthesized geometries revealed relationships between microstructural features, hydraulic permeability and mechanical properties. Empirical correlations were derived to predict longitudinal and transverse hydraulic permeability as functions of porosity, with R2 values of 0.98 and 0.97, respectively. Finite-element simulations examined mechanical behaviour under compression, showing stress concentrations at fibre cross-links and permeability reductions that varied with porosity and flow direction. These results led to a porosity-dependent model for normalized Young's modulus ([Formula: see text]). The proposed correlations and data augmentation technique aid in investigating structure-property relationships in meniscal tissue, potentially benefiting biomimetic implant design. This approach may help bridge data gaps where obtaining numerous real samples is impractical or unethical.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

End-to-end three-dimensional designing of complex disordered materials from limited data using machine learning

Precise 3D representation of complex materials, here the lithium-ion batteries, is a critical step toward designing optimized energy storage systems. One requires obtaining several such samples for a more accurate evaluation of uncertainty and variability, which in turn can be costly and time demanding. Using 3D models is crucial when it comes to evaluating the transport and heat capacity of batteries. Further, such models represent the microstructures more precisely where connectivity and heterogeneity can be detected. However, 3D images are hard to access, and the available images are often collected in two dimensions (2D). Such 2D images, on the other hand, are more accessible and often have higher resolution. In this paper, a deep learning method has been applied to take advantage of 2D images and build 3D models of heterogeneous materials through which more accurate characterization and physical evaluations can be achieved. While being trained using only 2D images, the proposed framework can be utilized to generate 3D images. The proposed method is applied to a few realistic 3D images of lithium-ion battery electrodes. The results indicate that the implemented method can reproduce important structural properties while the flow and heat properties are within an acceptable range.