Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization

2025/10/26 by Nobari, Amin Heyrani, Regenwetter, Lyle, Picard, Cyril +2
#Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #and Science (cs.CE)

paper · doi:10.48550/arxiv.2510.23667

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 & data can be found at https://github.com/ahnobari/OptimizeAnyTopology.

Citations

• Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
• Diffusing the Optimal Topology: A Generative Optimization Approach
• Continuous-Time Functional Diffusion Processes
• Score-based Diffusion Models in Function Space
• Diffusion Generative Models in Infinite Dimensions
• Topology Optimization via Machine Learning and Deep Learning: A Review
• Diffusion Models Beat GANs on Topology Optimization
• Classifier-Free Diffusion Guidance
• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
• Elucidating the Design Space of Diffusion-Based Generative Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
• Palette: Image-to-Image Diffusion Models
• Deep Generative Models in Engineering Design: A Review
• Generative Adversarial Networks
• Highly accurate protein structure prediction with AlphaFold
• Score-based Generative Modeling in Latent Space
• On Fast Sampling of Diffusion Probabilistic Models
• Cascaded Diffusion Models for High Fidelity Image Generation
• Diffusion Models Beat GANs on Image Synthesis
• Image Super-Resolution via Iterative Refinement
• Noise Estimation for Generative Diffusion Models
• Learning Continuous Image Representation with Local Implicit Image Function
• Score-Based Generative Modeling through Stochastic Differential Equations
• Denoising Diffusion Implicit Models
• Denoising Diffusion Probabilistic Models
• Improved Techniques for Training Score-Based Generative Models
• TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
• Generative Modeling by Estimating Gradients of the Data Distribution
• A Novel Topology Optimization Approach using Conditional Deep Learning
• Neural Discrete Representation Learning
• Proximal Policy Optimization Algorithms
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• Выпуклая оптимизация

Related

• Deep learning-based topological optimization for representing a user-specified design area
• Diffusing the Optimal Topology: A Generative Optimization Approach
• NITO: Neural Implicit Fields for Resolution-free Topology Optimization
• A mechanistic-based data-driven approach to accelerate structural topology optimization through finite element convolutional neural network (FE-CNN)
• TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain