无需重训练，可基于部分设计生成满足性能与参数约束的新方案。
RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints
- 用掩码重采样实现生成过程中的可控局部重绘
- 在船体和机翼设计任务中生成符合性能要求的新结构
- 适合需要快速迭代且保留关键设计部分的工程场景
本文提出一种RePaint增强框架，结合预训练的性能引导去噪扩散概率模型（DDPM），用于满足性能与参数约束的工程设计生成。该方法可在不重新训练模型的前提下，基于部分参考设计生成缺失的设计组件，并确保性能达标。通过推理过程中引入掩码重采样机制，实现对部分设计的高效、可控重绘，这是传统基于DDPM的方法所不具备的能力。在参数化船体设计和机翼设计两个代表性问题上进行了评估，结果表明该方法能根据部分参考设计生成具有预期性能的新设计，其精度与预训练模型相当或更优，同时可通过固定部分设计实现可控创新。整体上，该方法为工程领域提供了高效的、无需训练的参数约束感知生成设计解决方案。
原文摘要 · Abstract (English)
This paper presents a RePaint-enhanced framework that integrates a pre-trained performance-guided denoising diffusion probabilistic model (DDPM) for performance- and parameter-constraint engineering design generation. The proposed method enables the generation of missing design components based on a partial reference design while satisfying performance constraints, without retraining the underlying model. By applying mask-based resampling during inference process, RePaint allows efficient and controllable repainting of partial designs under both performance and parameter constraints, which is not supported by conventional DDPM-base methods. The framework is evaluated on two representative design problems, parametric ship hull design and airfoil design, demonstrating its ability to generate novel designs with expected performance based on a partial reference design. Results show that the method achieves accuracy comparable to or better than pre-trained models while enabling controlled novelty through fixing partial designs. Overall, the proposed approach provides an efficient, training-free solution for parameter-constraint-aware generative design in engineering applications.
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