Deep learning-aided inverse design of porous metamaterials

• pVAE-CNN framework enables fast inverse design of porous metamaterials with target hydraulic properties. • CNN predicts permeability directly from microstructure images, bypassing expensive LBM simulations. • Structured latent space allows intuitive exploration, interpolation, and property-driven optimization. • High reconstruction accuracy supports efficient, low-cost design of tailored microstructures. The ultimate aim of the study is to explore the inverse design of porous metamaterials using a deep learning-based generative framework. Specifically, we develop a property-variational autoencoder (pVAE), a variational autoencoder (VAE) augmented with a regressor, to generate structured metamaterials with tailored hydraulic properties, such as porosity and permeability. While this work uses the lattice Boltzmann method (LBM) to generate intrinsic permeability tensor data for limited porous microstructures, a convolutional neural network (CNN) is trained using a bottom-up approach to predict effective hydraulic properties. This significantly reduces the computational cost compared to direct LBM simulations. The pVAE framework is trained on two datasets: a synthetic dataset of artificial porous microstructures and CT-scan images of volume elements from real open-cell foams. The encoder-decoder architecture of the VAE captures key microstructural features, mapping them into a compact and interpretable latent space for efficient structure-property exploration. The study provides a detailed analysis and interpretation of the latent space, demonstrating its role in structure-property mapping, interpolation, and inverse design. This approach facilitates the generation of new metamaterials with desired properties. The datasets and codes used in this study are openly available at [ https://doi.org/10.25835/t799y2ji ] to support further research.