TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures

2026-02-25

AI Summary

This work addresses the challenge that post-hoc local modifications of topology-optimized structures often disrupt load paths, leading to abrupt performance degradation, while re-optimization is computationally expensive and yields unstable results. To overcome this, the authors propose a rapid editing framework based on the pre-trained topology foundation model OAT. Leveraging its structured latent embeddings, the method integrates a diffusion mechanism with partial noising and guided denoising to enable user-intent-driven, physics-aware edits. The approach generates globally coherent, identity-preserving, and mechanically consistent modifications in sub-second time, further refined by SIMP-based fine-tuning. This strategy significantly outperforms direct editing in density space, maintaining structural performance while avoiding catastrophic failure.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• topology optimization
• post-optimization editing
• structural compliance
• load path preservation
• late-stage design revision

Innovation

• topology optimization
• latent diffusion
• physics-aware editing
• foundation model
• post-optimization editing