标题： 带删失采样的拓扑设计：用人类偏好引导扩散 显示英文标题

标题： Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences

摘要： 最近在去噪扩散模型方面的进展使得拓扑优化中优化结构的快速生成成为可能。 然而，这些模型通常依赖于代理预测器来施加物理约束，这可能无法捕捉到对人类专家来说显而易见的细微但关键的设计缺陷，例如浮动部件或边界不连续性。 在本工作中，我们提出了一种新颖的人机协同扩散框架，该框架通过一个在少量人类反馈上训练的轻量级奖励模型引导生成过程。 受生成建模中偏好对齐技术的启发，我们的方法通过使用人类对齐奖励的梯度调节反向扩散轨迹，学习抑制不现实的输出。 具体而言，我们收集生成拓扑的二进制人类评估，并训练分类器来检测浮动材料和边界违规情况。 这些奖励模型随后被集成到预训练扩散生成器的采样循环中，引导其生成不仅结构性能良好，而且物理上合理且可制造的设计。 我们的方法是模块化的，不需要重新训练扩散模型。 初步结果表明，在各种测试条件下，故障模式显著减少，设计真实性得到提高。 这项工作弥合了自动化设计生成与专家判断之间的差距，为可信的生成设计提供了一个可扩展的解决方案。 显示英文摘要

摘要： Recent advances in denoising diffusion models have enabled rapid generation of optimized structures for topology optimization. However, these models often rely on surrogate predictors to enforce physical constraints, which may fail to capture subtle yet critical design flaws such as floating components or boundary discontinuities that are obvious to human experts. In this work, we propose a novel human-in-the-loop diffusion framework that steers the generative process using a lightweight reward model trained on minimal human feedback. Inspired by preference alignment techniques in generative modeling, our method learns to suppress unrealistic outputs by modulating the reverse diffusion trajectory using gradients of human-aligned rewards. Specifically, we collect binary human evaluations of generated topologies and train classifiers to detect floating material and boundary violations. These reward models are then integrated into the sampling loop of a pre-trained diffusion generator, guiding it to produce designs that are not only structurally performant but also physically plausible and manufacturable. Our approach is modular and requires no retraining of the diffusion model. Preliminary results show substantial reductions in failure modes and improved design realism across diverse test conditions. This work bridges the gap between automated design generation and expert judgment, offering a scalable solution to trustworthy generative design.