This work presents a diffusion transformer framework for data-driven structural topology optimization that combines the accuracy of physics-based methods with the efficiency of generative deep learning. Conventional approaches such as the Solid Isotropic Material with Penalization (SIMP) method require repeated finite element analyses at every iteration, making large-scale or real-time optimization computationally expensive. We propose a hybrid conditioning diffusion transformer (DiT) model that learns to generate near-optimal topologies directly from problem definitions, eliminating iterative analysis during inference. The model integrates spatially distributed conditioning through concatenated stress and strain fields and global conditioning via adaptive layer normalization (AdaLN) using scalar descriptors such as load position, magnitude, and prescribed volume fraction. A dataset of 30,000 two-dimensional SIMP-optimized structures was generated for training and evaluation. Results demonstrate that the proposed DiT achieves less than 1% compliance errors relative to ground-truth SIMP solutions while maintaining accurate volume fractions and structural connectivity. Deterministic DDIM sampling enables high-fidelity topology generation in seconds using as few as five denoising steps, enabling near-real-time performance. The hybrid conditioning diffusion transformer thus provides an efficient and scalable alternative to traditional topology optimization methods, with strong potential for integration into interactive computer-aided design workflows.

翻译：本文提出了一种基于扩散变换器的数据驱动结构拓扑优化框架，该框架将基于物理的方法的准确性与生成式深度学习的高效性相结合。传统的固体各向同性材料惩罚（SIMP）方法需要在每次迭代中进行重复的有限元分析，这使得大规模或实时优化的计算成本高昂。我们提出了一种混合条件扩散变换器（DiT）模型，该模型能够直接从问题定义中学习生成接近最优的拓扑结构，从而在推理过程中消除迭代分析。该模型通过拼接的应力和应变场实现空间分布式条件，并通过自适应层归一化（AdaLN）利用标量描述符（如载荷位置、大小和指定体积分数）实现全局条件。我们生成了一个包含30,000个二维SIMP优化结构的数据集用于训练和评估。结果表明，所提出的DiT相对于地面真实SIMP解实现了小于1%的柔度误差，同时保持了准确的体积分数和结构连通性。确定性DDIM采样仅需少至五个去噪步骤即可在几秒内生成高保真拓扑，从而实现近实时性能。因此，混合条件扩散变换器为传统拓扑优化方法提供了一种高效且可扩展的替代方案，具有融入交互式计算机辅助设计工作流的巨大潜力。