mlsanityData to 7 Oct 2026

Latent Diffusion Models for Structural Component Design

arXiv 2309.11601 ↗First posted 20 Sep 2023cs.LGcited by 4

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions. One of the distinct advantages our approach offers over other generative approaches, such as generative adversarial networks (GANs), is that it permits the editing of existing designs. We train our model using a dataset of geometries obtained from structural topology optimization utilizing the SIMP algorithm. Consequently, our framework generates inherently near-optimal designs. Our work presents quantitative results that support the structural performance of the generated designs and the variability in potential candidate designs. Furthermore, we provide evidence of the scalability of our framework by operating over voxel domains with resolutions varying from 323 to 1283. Our framework can be used as a starting point for generating novel near-optimal designs similar to topology-optimized designs.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 18 of its 54 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

18 of 54 references.

References · 18

  1. 2022Classifier-Free Diffusion Guidancecited by 5,717
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2017Neural Discrete Representation Learningcited by 4,650
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  7. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  8. 2022Diffusion Models Beat GANs on Topology Optimizationcited by 79
  9. 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
  10. 2018Deep learning for determining a near-optimal topological design without any iterationcited by 40
  11. 2017Neural networks for topology optimizationcited by 39
  12. 2022On the use of Artificial Neural Networks in Topology Optimisationcited by 35
6 more
  1. 20183D Topology Optimization using Convolutional Neural Networkscited by 27
  2. 2019A Novel Topology Optimization Approach using Conditional Deep Learningcited by 17
  3. 2019A deep Convolutional Neural Network for topology optimization with strong generalization abilitycited by 16
  4. 2019Topology Optimization under Uncertainty using a Stochastic Gradient-based Approachcited by 6
  5. 2019Convolutional Neural Network-based Topology Optimization (CNN-TO) By Estimating Sensitivity of Compliance from Material Distributioncited by 4
  6. 2020Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimizationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Solving engineering eigenvalue problems with neural networks using the Rayleigh quotientcited by 8
  2. 2026TabPFN for Zero-shot Parametric Engineering Design Generationcited by 0
  3. 2026Adversarial Agents on Topology Optimization: Understanding the Fragility and Robustness of Deep Learning-based and Physics-Based Design Models under Adversarial Perturbationcited by 0
  4. 2026Conditional Flow Matching for ML-Based Inverse Design Problemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
  2. 2023Diffusing the Optimal Topology: A Generative Optimization Approach
  3. 2022Elucidating the Design Space of Diffusion-Based Generative Models
  4. 2023On the Noise Scheduling for Generating Plausible Designs with Diffusion Models
  5. 2024Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields
  6. 2026Zero-shot rib design: merging training-free generative prior with topology optimization
  7. 2024Structured Pattern Expansion with Diffusion Models
  8. 2025Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences