# Physics-Guided Generative AI for Property-Targeted 3D Porous Media Design

arXiv: https://arxiv.org/abs/2607.24274 · HTML: https://mlsanity.com/paper/2607.24274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems](https://mlsanity.com/paper/2006.15274/) (2020)
- [MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN](https://mlsanity.com/paper/2210.06541/) (2022)
- [DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets](https://mlsanity.com/paper/2303.03403/) (2023)
- [Controlled Latent Diffusion Models for 3D Porous Media Reconstruction](https://mlsanity.com/paper/2503.24083/) (2025)
- [Deep learning-aided inverse design of porous metamaterials](https://mlsanity.com/paper/2507.17907/) (2025)
- [Using Physics Informed Generative Adversarial Networks to Model 3D porous media](https://mlsanity.com/paper/2409.11541/) (2024)

## Abstract (arXiv)

Inverse design of three-dimensional porous media is central to applications in filtration, catalysis, energy storage, fuel cells, thermal management, and biomedical scaffolds, but remains challenging because many distinct pore geometries can share similar porosity or permeability while small structural changes can strongly affect transport behaviour. This paper proposes a physics-guided generative AI framework for property-targeted porous media design, combining a property-aware variational autoencoder, a conditional latent diffusion model, and an independently trained differentiable structure-to-property surrogate. The framework learns a compact, physically informative latent design space, generates porous structures conditioned on target porosity and directional permeability, and refines generated samples using property-level feedback during denoising and decoding. Experiments on procedurally generated structures and real micro-CT porous-media datasets show improved target-property matching, directional permeability control, and property correlation compared with representative property-aware variational-autoencoder and latent-diffusion baselines. The results demonstrate a scalable route towards controllable inverse design of complex porous geometries and establish a foundation for simulation-informed generative AI tools in engineering and advanced materials discovery.