Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Topology Optimization
Amin Heyrani Nobari, Lyle Regenwetter, Cyril Picard, Ligong Han, Faez Ahmed
Structural topology optimization (TO) is essential in engineering design, enabling efficient material distribution for optimized physical performance.  As such, it serves as an exemplary representative challenge, encompassing stringent performance goals, hard constraints, and substantial nonlinearities typical of real-world engineering design and physics-based optimization problems. However, existing deep-learning solutions are confined to fixed low-resolution square grids, a few hand-coded boundary conditions and loads, and post-processing with gradient optimizers, which limits practical deployment. We present Optimize Any Topology (OAT), the first foundation model that predicts minimum-compliance layouts directly for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines an autoencoder with an implicit neural-field decoder, all trained on a new open corpus of 2.2 million optimized structures spanning 2M unique boundary-condition configurations we introduce called the OpenTO dataset. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 s inference on a single GPU across resolutions from 64×64 to 256x256 and aspect ratios as high as 10:1. These results establish a way for fast, shape and resolution-free general topology optimization and provide a large-scale dataset to spur further research in physics-aware generative modeling.