Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free
  Structural Topology Optimization
Authors: name: Amin Heyrani Nobari, name: Lyle Regenwetter, name: Cyril Picard, name: Ligong Han, name: Faez Ahmed
Organization: OpenTO

Structural topology optimization (TO) is central to engineering design but
remains computationally intensive due to complex physics and hard constraints.
Existing deep-learning methods are limited to fixed square grids, a few
hand-coded boundary conditions, and post-hoc optimization, preventing general
deployment. We introduce Optimize Any Topology (OAT), a foundation-model
framework that directly predicts minimum-compliance layouts for arbitrary
aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines
a resolution- and shape-agnostic autoencoder with an implicit neural-field
decoder and a conditional latent-diffusion model trained on OpenTO, a new
corpus of 2.2 million optimized structures covering 2 million unique
boundary-condition configurations. 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 second inference on a single GPU across
resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These
results establish OAT as a general, fast, and resolution-free framework for
physics-aware topology optimization and provide a large-scale dataset to spur
further research in generative modeling for inverse design. Code & data can be
found at https://github.com/ahnobari/OptimizeAnyTopology.