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

• URL: http://arxiv.org/abs/2602.00384v1
• Date: Fri, 30 Jan 2026 23:04:05 GMT
• Title: RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints
• Authors: Ke Wang, Nguyen Gia Hien Vu, Yifan Tang, Mostafa Rahmani Dehaghani, G. Gary Wang,
• Abstract summary: This paper presents a RePaint-enhanced framework that integrates a pre-trained performance-guided denoising diffusion probabilistic model (DDPM)<n>The proposed method enables the generation of missing design components based on a partial reference design without retraining the underlying model.<n>The framework is evaluated on two representative design problems, ship hull design and airfoil design.
• Score: 5.694916746852312
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• TabPFN for Zero-shot Parametric Engineering Design Generation [8.681307193373241]
  We propose a zero-shot generation framework for parametric engineering design based on TabPFN.<n>The proposed method generates design parameters sequentially conditioned on target performance indicators.<n>Compared with diffusion-based generative models, the proposed framework significantly reduces computational overhead and data requirements.
  arXiv  Detail & Related papers  (2026-02-02T19:51:40Z)
• Partial Inverse Design of High-Performance Concrete Using Cooperative Neural Networks for Constraint-Aware Mix Generation [2.08099858257632]
  High-performance concrete requires complex mix design decisions involving interdependent variables and practical constraints.<n>This study proposes a cooperative neural network framework for the partial inverse design of high-performance concrete.
  arXiv  Detail & Related papers  (2025-12-07T12:14:56Z)
• A Reward-Directed Diffusion Framework for Generative Design Optimization [0.0]
  This study builds on a fine-tuned diffusion model and reward-directed sampling to generate high-performance engineering designs.<n>The proposed framework generates samples that extend beyond the training data distribution, resulting in a greater 25 percent reduction in resistance for ship design and over 10 percent improvement in the lift-to-drag ratio for the 2D airfoil design.
  arXiv  Detail & Related papers  (2025-08-02T22:19:02Z)
• Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem [0.0]
  This study presents a methodology to treat performance-based seismic design as an inverse engineering problem.<n>By implementing explainable machine learning models, this methodology directly maps design variables and performance metrics.<n>The developed methodology is then applied to two different inventories of steel and concrete moment frames in Los Angeles and Charleston.
  arXiv  Detail & Related papers  (2025-08-01T03:08:19Z)
• SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation [52.6922833948127]
  In this work, we investigate the importance of parameters in pre-trained diffusion models.<n>We propose a novel model fine-tuning method to make full use of these ineffective parameters.<n>Our method enhances the generative capabilities of pre-trained models in downstream applications.
  arXiv  Detail & Related papers  (2024-09-10T16:44:47Z)
• Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints [53.66698106829144]
  We propose a unified model to handle a broad range of layout generation tasks. The model is based on continuous diffusion models. Experiment results show that LACE produces high-quality layouts.
  arXiv  Detail & Related papers  (2024-02-07T11:12:41Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation [17.164961143132473]
  We introduce a learning framework that demonstrates the efficacy of aligning the sampling trajectory of diffusion models with the optimization trajectory derived from traditional physics-based methods. Our method allows for generating feasible and high-performance designs in as few as two steps without the need for expensive preprocessing, external surrogate models, or additional labeled data. Our results demonstrate that TA outperforms state-of-the-art deep generative models on in-distribution configurations and halves the inference computational cost.
  arXiv  Detail & Related papers  (2023-05-29T09:16:07Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• Inverse design of photonic devices with strict foundry fabrication constraints [55.41644538483948]
  We introduce a new method for inverse design of nanophotonic devices which guarantees that designs satisfy strict length scale constraints. We demonstrate the performance and reliability of our method by designing several common integrated photonic components.
  arXiv  Detail & Related papers  (2022-01-31T02:27:25Z)
• MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization [13.866787416457454]
  Multi-objective optimization is key to solving many Engineering Design problems. Deep generative models can learn compact design representations. Mo-PaDGAN adds a Determinantal Point Processes based loss function to the generative adversarial network.
  arXiv  Detail & Related papers  (2020-09-15T13:58:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.