Abstract Recent advances in denoising diffusion models have enabled the rapid generation of optimized structures for topology optimization. However, these machine-learning (ML) 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-guided 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, does not require retraining the diffusion model, and can be readily integrated with existing ML-based topology optimization. The results show substantial reductions in failure modes and improved design realism across diverse test conditions. This work bridges the gap between AI-driven design generation and human experts’ judgments, offering a scalable solution to reliable generative design.
Kim et al. (Sun,) studied this question.