TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures

TL;DR

TopoEdit is presented, 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.

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.

Figures (12)

• Figure 1: TopoEdit uses a pretrained OAT with frozen weights. Given a reference topology and a user-specified desired edit (topology warp, lattice infill replacement, or no-design region enforcement), TopoEdit encodes the input into OAT’s structured latent, applies partial noising, and converts the edit into a corresponding latent-space intervention to produce an edited initialization. Starting from the edited latent, we run diffusion denoising with consistency-preserving guidance to recover an edited clean latent that preserves the instance identity away from the edit while allowing localized structural adaptation. Finally, the edited latent is decoded to yield the edited topology.
• Figure 2: Illustration of a warp edit. A green handle point is selected on the topology, and a red arrow represents a warp vector. The vector’s magnitude determines the target’s location (red cross), indicating the handle point’s movement. The percentage difference in distance between the green point and the target location is calcualted Distance Error (DE). Additionally, the Compliance Error (CE) can be computed. (b) Direct warp: When the warp is performed directly on the topology, the edit causes significant local deformation, while the distance error remains relatively small. However, the compliance error increases as straight members become distorted. SIMP post-processing partially reverts some of the local changes, which reduces the distance error. Latent warp: In this case, the edit is reflected on the global topology, where post-processing has not made substantial changes to the topology. As a result, the topology remains stable, and the distance error is smaller.
• Figure 3: TopoEdit workflow for warping the topology. The topology is first converted into latent $z_0$, then partial noise is added $z_\tau$, then the warp operation is mapped onto the latent space $z_\tau'$, then the warped latent is denoised to obtain the latent $z_0'$. Decoding $z_0'$ gives the edited topology.
• Figure 4: We select examples from the warp experiments to visualize the direct and latent-based edit. All edit results shown are after 10 steps of SIMP post-processing. The last three example is from the testing set. Left column: the reference topology. The warp edit is placed on a joint and shown as a red arrow with the end located at the point where the warp is applied and the arrow head pointing towards the target edit direction. Middle column: applying the warp edit directly to the topology, after SIMP post-processing, a larger deviation from the reference topology occurs. We show the closest joint point to the target edit location as a green dot and the edit target location as a red cross. In cases with large changes in topology after post-processing, the closest joint location is located on a different edge completely. The abrupt changes caused by the direct warp cause the post-processing optimizer to alter the topology and connectivity. Right column: with latent-based warp edits, the overall appearance of the structure is better preserved, resulting in smaller error compliance and a closer distance to the target location.
• Figure 5: Applying the edit (a), direct warp (b) is deterministic and produces a single result. Latent warp (c) based on the diffusion model is stochastic; the same edit on the latent can produce different results. The number of results generated can be selected as one of the configuration parameters. The stochastic nature of the diffusion model allows the user to explore a distribution of possible outcomes.