{"ID":2850067,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23667","arxiv_id":"2510.23667","title":"Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization","abstract":"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 \u0026 data can be found at https://github.com/ahnobari/OptimizeAnyTopology.","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2510.23667","url_pdf":"https://arxiv.org/pdf/2510.23667v1","authors":"[\"Amin Heyrani Nobari\",\"Lyle Regenwetter\",\"Cyril Picard\",\"Ligong Han\",\"Faez Ahmed\"]","published":"2025-10-26T15:11:54Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CE\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":607763,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2850067,"paper_url":"https://arxiv.org/abs/2510.23667","paper_title":"Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization","repo_url":"https://github.com/ahnobari/OptimizeAnyTopology","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}