Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free
  Structural Topology Optimization
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.
69020e82646208eac0d1f646
Amin Heyrani Nobari
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2025-10-30T14:41:28.748Z
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Amin Heyrani Nobari
ahn1376
user
ahn1376
69020e82646208eac0d1f647
Lyle Regenwetter
69020e82646208eac0d1f648
Cyril Picard
69020e82646208eac0d1f649
Ligong Han
69020e82646208eac0d1f64a
Faez Ahmed
Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free
  Structural Topology Optimization
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.
OAT, a deep-learning framework combining autoencoder, neural-field decoder, and latent-diffusion model, achieves fast and general topology optimization with high performance across various conditions and resolutions.
autoencoder implicit neural-field decoder conditional latent-diffusion model OpenTO topology optimization mean compliance inference resolution-free physics-aware