Latent Space Diffusion for Topology Optimization

TL;DR

This paper introduces a novel latent diffusion model combined with variational autoencoders for fast, physically-guided topology optimization, outperforming existing methods in accuracy and manufacturability.

Contribution

It presents a new framework that integrates LDMs and VAEs conditioned on physical fields for efficient topology generation, addressing scalability and realism issues.

Findings

Outperforms existing diffusion methods in compliance accuracy

Achieves better volume control and structural connectivity

Provides a scalable, robust alternative to traditional methods

Abstract

Topology optimization enables the automated design of efficient structures by optimally distributing material within a defined domain. However, traditional gradient-based methods often scale poorly with increasing resolution and dimensionality due to the need for repeated finite element analyses and sensitivity evaluations. In this work, we propose a novel framework that combines latent diffusion models (LDMs) with variational autoencoders (VAEs) to enable fast, conditional generation of optimized topologies. Unlike prior approaches, our method conditions the generative process on physically meaningful fields, specifically von Mises stress, strain energy density, volume fraction, and loading information, embedded as dense input channels. To further guide the generation process, we introduce auxiliary loss functions that penalize floating material, load imbalance, and volume fraction deviation, thereby encouraging physically realistic and manufacturable designs. Numerical experiments on a large synthetic dataset demonstrate that our VAE-LDM framework outperforms existing diffusion-based methods in compliance accuracy, volume control, and structural connectivity, providing a robust and scalable alternative to conventional