Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences

TL;DR

This paper introduces a human-in-the-loop diffusion sampling method that uses minimal human feedback to guide topology design, reducing unrealistic features and improving physical plausibility without retraining the diffusion model.

Contribution

It presents a novel framework that incorporates human preferences into diffusion-based topology optimization through lightweight reward models, enhancing design realism and reliability.

Findings

Significant reduction in design failure modes

Improved physical plausibility of generated structures

No need for retraining the diffusion model

Abstract

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.