TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures
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Abstract (arXiv)
Despite topology optimization producing high-performance structures, late-stage localized revisions remain brittle: direct density-space edits (e.g., warping pixels, inserting holes, swapping infill) can sever load paths and sharply degrade compliance, while re-running optimization is slow and may drift toward a qualitatively different design. We present TopoEdit, a fast post-optimization editor that demonstrates how structured latent embeddings from a pre-trained topology foundation model (OAT) can be repurposed as an interface for physics-aware engineering edits. Given an optimized topology, TopoEdit encodes it into OAT's spatial latent, applies partial noising to preserve instance identity while increasing editability, and injects user intent through an edit-then-denoise diffusion pipeline. We instantiate three edit operators: drag-based topology warping with boundary-condition-consistent conditioning updates, shell-infill lattice replacement using a lattice-anchored reference latent with updated volume-fraction conditioning, and late-stage no-design region enforcement via masked latent overwrite followed by diffusion-based recovery. A consistency-preserving guided DDIM procedure localizes changes while allowing global structural adaptation; multiple candidates can be sampled and selected using a compliance-aware criterion, with optional short SIMP refinement for warps. Across diverse case studies and large edit sweeps, TopoEdit produces intention-aligned modifications that better preserve mechanical performance and avoid catastrophic failure modes compared to direct density-space edits, while generating edited candidates in sub-second diffusion time per sample.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 66 references that are in the map. First posted on arXiv 25 Feb 2026.
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24 of 66 references.
References · 24
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
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- 2024DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editingcited by 88
12 more
- 2022Diffusion Models Beat GANs on Topology Optimizationcited by 79
- 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
- 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generationcited by 51
- 2024Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagationcited by 50
- 2018Deep learning for determining a near-optimal topological design without any iterationcited by 40
- 2023FreeDrag: Feature Dragging for Reliable Point-based Image Editingcited by 28
- 2024FastDrag: Manipulate Anything in One Stepcited by 20
- 2019A Novel Topology Optimization Approach using Conditional Deep Learningcited by 17
- 2021Length Scale Control in Topology Optimization using Fourier Enhanced Neural Networkscited by 10
- 2024NITO: Neural Implicit Fields for Resolution-free Topology Optimizationcited by 3
- 2025Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimizationcited by 2
- 2020A Survey on Patch-based Synthesis: GPU Implementation and Optimizationcited by 1
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- 2026Zero-shot rib design: merging training-free generative prior with topology optimization
- 2025Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences
- 2026Trajectory-Aware Flow Matching for Topology Optimisation
- 2026HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization
- 2026On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching
- 2023Diffusing the Optimal Topology: A Generative Optimization Approach
- 2026TopoMamba: A Load-Support Relation-Guided Multi-Directional State-Space Model for Topology Optimization