RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints

TL;DR

This paper introduces RePaint-enhanced diffusion models that generate constrained engineering designs from partial references without retraining, enabling efficient, controllable, and performance-aware design generation.

Contribution

It proposes a novel mask-based RePaint framework integrated with pre-trained diffusion models for constrained design generation without retraining.

Findings

Achieves comparable or better accuracy than existing models.

Enables controlled design novelty through partial design fixing.

Demonstrates effectiveness on ship hull and airfoil design problems.

Abstract

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.