Topology optimization can generate high-performance structures, but designers often need to revise the resulting topology in ways that reflect fabrication preferences, structural intuition, or downstream design constraints. In particular, they may wish to explicitly control interpretable structural characteristics such as member thickness, characteristic member length, the number of joints, or the number of members connected to a joint. These quantities are often discrete, non-smooth, or only available through a forward evaluation procedure, making them difficult to impose within conventional optimization pipelines. We present TopoCtrl, a post-optimization control framework that repurposes the latent space of a pre-trained topology foundation model for explicit characteristic-guided editing. Given an optimized topology, TopoCtrl encodes it into the latent space of a latent diffusion model, applies partial noising to preserve instance similarity while creating room for modification, and then performs regression-guided denoising toward a prescribed target characteristic. The concept is to train a lightweight regression model on latent representations annotated with evaluated structural characteristics, and to use its gradient as a differentiable guidance signal during reverse diffusion. This avoids the need for characteristic-specific reformulations, hand-derived sensitivities, or iterative optimization. Because the method operates through partial noising of an existing topology latent, it preserves overall structural similarity while still enabling characteristic controls. Across representative control tasks involving both continuous and discrete structural characteristics, TopoCtrl produces target-aligned topology modifications while better preserving structural coherence and design intent than indirect parameter tuning or naive geometric post-processing.

翻译：拓扑优化能够生成高性能的结构，但设计者通常需要以反映制造偏好、结构直觉或下游设计约束的方式修改所得到的拓扑。具体而言，他们可能希望显式控制可解释的结构特征，例如构件厚度、特征构件长度、节点数量，或连接到某个节点的构件数量。这些量通常是离散的、非光滑的，或只能通过正向评估过程获得，这使得在传统优化框架中施加控制困难重重。我们提出TopoCtrl，一种后优化控制框架，该框架重新利用预训练拓扑基础模型的隐空间，以实现显式的特征引导编辑。给定一个优化后的拓扑，TopoCtrl将其编码到潜在扩散模型的隐空间中，应用部分加噪以保持实例相似性同时为修改留出空间，然后执行回归引导的去噪过程，朝向预设的目标特征。其核心思想是：在标注了已评估结构特征的隐表示上训练一个轻量级回归模型，并在逆向扩散过程中利用其梯度作为可微分的引导信号。这避免了对特征特定的重新公式化、手工推导的灵敏度或迭代优化的需求。由于该方法通过对现有拓扑隐变量进行部分加噪来运作，它能够在实现特征控制的同时保持整体结构相似性。在涉及连续和离散结构特征的代表性控制任务中，与间接参数调整或朴素的几何后处理相比，TopoCtrl在产生与目标对齐的拓扑修改的同时，能更好地保持结构连贯性和设计意图。