TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures

TL;DR

TopoEdit is a fast, physics-aware editing tool for topology-optimized structures that uses a pre-trained foundation model to enable localized, intention-aligned modifications with minimal performance degradation.

Contribution

It introduces a novel diffusion-based editing framework leveraging a pre-trained topology foundation model for fast, structure-preserving modifications of optimized designs.

Findings

Produces intention-aligned modifications with better performance preservation.

Operates in sub-second diffusion time per sample.

Avoids catastrophic failure modes compared to direct density edits.

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.