mlsanityData to 7 Oct 2026

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

arXiv 2607.24274 ↗First posted 27 Jul 2026cs.LG cs.AIcited by 0

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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.

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References · 7

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2020Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systemscited by 51
  3. 2022MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGANcited by 5
  4. 2023DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data setscited by 5
  5. 2025Controlled Latent Diffusion Models for 3D Porous Media Reconstructioncited by 5
  6. 2025Deep learning-aided inverse design of porous metamaterialscited by 4
  7. 2024Using Physics Informed Generative Adversarial Networks to Model 3D porous mediacited by 3

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  3. 2026Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design
  4. 2025MIND: Microstructure INverse Design with Generative Hybrid Neural Representation
  5. 2026Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
  6. 2025Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
  7. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
  8. 2025MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation