发表机构
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉、语音和信号处理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对三维多孔介质逆向设计难题，提出物理引导生成式人工智能框架，结合多种模型学习潜在设计空间，依目标属性生成并优化结构，实验表明该方法能提升目标属性匹配等，为复杂多孔几何形状可控逆向设计及相关工具奠定基础。
AI 中文摘要
三维多孔介质的逆向设计在过滤、催化、储能、燃料电池、热管理和生物医学支架等应用中至关重要，但具有挑战性，因为许多不同的孔隙几何形状可具有相似的孔隙率或渗透率，且小的结构变化会强烈影响传输行为。本文提出了一种用于属性靶向多孔介质设计的物理引导生成式人工智能框架，结合了属性感知变分自编码器、条件潜在扩散模型和独立训练的可微结构到属性代理。该框架学习紧凑、物理信息丰富的潜在设计空间，根据目标孔隙率和方向渗透率生成多孔结构，并在去噪和解码期间使用属性级反馈优化生成的样本。与代表性的属性感知变分自编码器和潜在扩散基线相比，在程序生成的结构和真实微CT多孔介质数据集上的实验显示出更好的目标属性匹配、方向渗透率控制和属性相关性。结果证明了一条通往复杂多孔几何形状可控逆向设计的可扩展途径，并为工程和先进材料发现中的模拟知情生成式人工智能工具奠定了基础。
英文摘要
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.
Comments15 pages, 8 figures
