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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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pipeline_tag: image-to-image |
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datasets: |
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- OpenTO/OpenTO |
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--- |
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# Optimize Any Topology (OAT) |
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This repository contains the official implementation of **Optimize Any Topology (OAT)**, a foundation-model framework for shape- and resolution-free structural topology optimization. OAT directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. It combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model. |
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**Paper**: [Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization](https://huggingface.co/papers/2510.23667) |
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**Code**: https://github.com/ahnobari/OptimizeAnyTopology |
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## Abstract |
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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 this https URL. |
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## The Open-TO Dataset |
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The OpenTO dataset, which was used for training OAT, is publicly available on the Hugging Face Hub at [OpenTO/OpenTO](https://huggingface.co/datasets/OpenTO/OpenTO). |
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## Pretrained Checkpoints |
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Pretrained checkpoints for both the Neural Field Auto-Encoder (NFAE) and Latent Diffusion Model (LDM) components of OAT are available on the Hugging Face Hub: |
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- Auto Encoder: [OpenTO/NFAE](https://huggingface.co/OpenTO/NFAE) |
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- Latent Diffusion: [OpenTO/LDM](https://huggingface.co/OpenTO/LDM) |
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- Auto Encoder Large Latent: [OpenTO/NFAE_L](https://huggingface.co/OpenTO/NFAE_L) |
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- Latent Diffusion Large Latent: [OpenTO/LDM_L](https://huggingface.co/OpenTO/LDM_L) |
