[
    {
        "id": "osp-24619",
        "type": "article-journal",
        "title": "ReGDiff: Guided Diffusion in Regulated Latent Space for Exploring Metamaterial Voxel Geometry",
        "author": [
            {
                "family": "Zhan",
                "given": "Wangzhi"
            },
            {
                "family": "Chen",
                "given": "Jianpeng"
            },
            {
                "family": "Fu",
                "given": "Dongqi"
            },
            {
                "family": "Zhou",
                "given": "Dawei"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/regdiff-guided-diffusion-in-regulated-latent-space-for-exploring-metamaterial-voxel-geometry",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Metamaterials are artificially engineered structures whose mechanical and physical behaviors are strongly shaped by geometry rather than composition. Voxel representation provides a unified format for metamaterial geometry generation, as it can express diverse classes such as truss, shell, and porous structures within a single cubic discretization. However, voxel-based generation faces a plausibility-novelty trade-off: staying close to known geometries helps preserve geometric regularities, while moving away from them is necessary for novelty but may produce degenerate geometries. To address this challenge, we propose REGDIFF, a generative framework that couples voxel representation with latent space regulation and guided diffusion. REGDIFF introduces a repel-and-sink (RAS) mechanism to smooth the latent distribution of plausible geometries, and short-range repulsion (SRR) guidance to discourage generation overly close to known samples while maintaining geometric plausibility. We further contribute a voxel-based benchmark covering truss- and shell-type metamaterial geometries, together with an evaluation module for geometric plausibility, novelty, and diversity. Experiments show that REGDIFF outperforms voxel-based generative baselines, achieving +8.9% in geometric plausibility, +46.4% in novelty, and +128.6% in diversity on average across two datasets. These results suggest that REGDIFF is a strong geometry candidate generator for downstream evaluation. Our code is provided at https://github.com/wzhan24/ReGDiff."
    }
]