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Latent Diffusion Models for Structural Component Design (2309.11601v2)

Published 20 Sep 2023 in cs.LG

Abstract: 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.

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Citations (4)
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Summary

  • The paper introduces a generative design framework that leverages Latent Diffusion Models and a dual-headed VAE to generate diverse structural component designs.
  • It conditions the latent space using initial strain energy and SIMP-optimized geometries, enabling the generation of multiple design variants from a single input.
  • Performance metrics such as cosine similarity and volume fraction deviations confirm enhanced design uniqueness and structural robustness over traditional methods.

Latent Diffusion Models for Structural Component Design

Introduction

The paper introduces a novel framework for structural component design leveraging Latent Diffusion Models (LDMs). Unlike traditional methods that focus heavily on using procedural modeling and deterministic optimization techniques, this approach utilizes generative modeling to improve design exploration capabilities, particularly in the context of manufacturing and engineering design phases. The choice of LDMs offers notable advantages over GANs and other traditional generative models by mitigating mode collapse and enabling efficient design editing of pre-existing geometries.

Framework Overview

The framework consists of a Multi-Headed Variational Autoencoder (VAE) and a Diffusion Model (DM) operating in the VAE's latent space. The VAE processes the Initial Strain Energy and SIMP-Optimized design geometries to create latent representations. The DM then generates new design variations conditioned on initial conditions, represented as latent codes, effectively allowing the creation of diverse candidate designs from a single input scenario. Figure 1

Figure 1: An overview of training the external Multi-Headed VAE.

Multi-Headed Variational Autoencoder

The external VAE is crucial to the framework, incorporating dual encoder networks—a pathway which encodes Initial Strain Energy into a latent space and another which processes SIMP-Optimized designs. Both pathways contribute to a combined latent representation that the VAE's decoder network subsequently converts back to voxel space, thus reconstructing an approximate SIMP geometry. This dual-headed approach allows integration of conditioning factors into the encoding process directly. Figure 2

Figure 2: An overview of training the Latent Diffusion Model.

Latent Diffusion Model Training

Within the latent space defined by the VAE, the DM iteratively refines candidate design representations. This allows each latent vector to map to various design outcomes depending on the initial random noise input. Figure 3 exemplifies the emergent refinement of initial random Gaussian samples across inference iterations, quantitatively demonstrating improvements in structural features over traditional VAE architectures. Figure 3

Figure 3: Decoded posteriors from the DDPM denoising trajectory during inference. Decoding the initial random Gaussian sample (far left design) highlights the improvement our framework provides over a traditional VAE.

Performance Evaluation

Performance metrics focus on design uniqueness and structural robustness. Figure 4 presents different design variants generated by the model for identical input strain energy conditions, exhibiting variability and suggesting enhanced exploration capability over deterministic methods. Figure 4

Figure 4: Multiple candidate designs for a given Initial Strain Energy Map. From left to right, Initial Strain Energy, SIMP-Optimized Design, and four different generated designs by the proposed framework.

Statistical measures such as cosine similarity and volume fraction deviations provide additional quantitative insight into the generated designs' variability and comparability against SIMP-optimized counterparts (Figures 5 and 6). Figure 5

Figure 5: The distribution of cosine similarity between the SIMP-Optimized design and LDM-Generated designs for a given Initial Strain Energy.

Figure 6

Figure 6: The distributions of volume fractions for SIMP-Optimized designs and LDM-Generated designs.

Discussion and Future Directions

The framework successfully demonstrates the generative capabilities of LDMs in producing multiple plausible structural component designs, thereby broadening the conceptual design phase's scope. Moreover, the potential to operate effectively over 3D voxel domains emphasizes scalability. Future research may explore more advanced conditioning methods, such as user-defined constraints directly influencing volume or structural parameters, enhancing the flexibility and applicability of generative design processes in engineering contexts.

Conclusion

This framework marks a significant advancement in application-specific generative design, emphasizing LDMs' robustness in the design ideation phase. It offers a computationally viable approach to generating near-optimal and diverse design solutions, setting the stage for future exploration and refinement in AI-driven design methodologies.

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