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The aim is to enhance topology optimization by incorporating human feedback into machine learning models for better design outcomes.

A Human-Guided Generative Model Based on Diffusion Processes for Topology Optimization

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The aim is to enhance topology optimization by incorporating human feedback into machine learning models for better design outcomes.
Developed a human-guided diffusion framework using minimal human feedback to train a lightweight reward model.
Collected binary human evaluations to identify design flaws like floating material and boundary violations.
Integrated reward models into the diffusion generator's sampling loop to guide design outputs.
Significantly reduced failure modes in generated designs, leading to improved structural performance.
Achieved high design realism across various test conditions, enhancing manufacturability.
Demonstrated scalable integration with existing machine learning-based topology optimization tools.

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Randomized trial demonstrates improved design realism in topology optimization, indicating scalable AI-human collaboration.

Kim et al. (2026) studied this question.

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