OAT3D: A Generative Model for 3D Shape- and Resolution-Free Structural Topology Optimization

Topology optimization determines how material should be distributed within a design domain to optimize structural performance under prescribed loads, supports, and constraints. The problems are highly nonconvex, and conventional solvers require repeated finite element analyses, making high-resolution 3D topology optimization computationally expensive. Data-driven approaches face an additional bottleneck: generating training datasets requires solving large numbers of these costly problems, so existing work in data-driven topology optimization primarily focuses on 2D problems. Large-scale level 3D datasets remain scarce, and existing models remain limited in problem scale and boundary-condition diversity. Here, we introduce Optimize Any Topology-3D (OAT3D), a generative model for high-resolution 3D structural topology optimization. OAT3D introduces a novel sparse problem encoding to represent load and fixity conditions and combines a sparse volumetric variational autoencoder with a conditional diffusion transformer. We construct a dataset of 501,865 optimized topologies, generated using 51,800 GPU-hours and encompassing structures with up to one million elements and substantially more complex loading and support configurations than any existing structural topology optimization dataset. To our knowledge, this is the largest dataset for 3D topology optimization structures. Trained on this dataset, OAT3D generates structures at 512${}^3$ resolution and achieves a 45-fold speedup over conventional topology optimization. These contributions expand the scale and diversity of data-driven topology optimization for three-dimensional structural design.