优化任意拓扑：一种形状与分辨率无关的结构拓扑优化基础模型

摘要

结构拓扑优化是工程设计的核心环节，但由于复杂的物理约束和硬性条件限制，其计算过程始终面临高强度挑战。现有深度学习方法受限于固定方形网格、少量手动编码的边界条件以及后验优化模式，难以实现通用化部署。我们提出"任意拓扑优化"框架，该基础模型能直接预测任意长宽比、分辨率、体积分数、载荷及约束条件下的最小柔度构型。OAT融合了分辨率与形状无关的自编码器、隐式神经场解码器，以及基于OpenTO数据集训练的条件潜空间扩散模型——该新型数据库包含220万个优化结构，覆盖200万种独特边界条件配置。在四项公开基准测试和两项高难度未知场景测试中，OAT相较于现有最优模型将平均柔度降低达90%，并在单GPU上实现64×64至256×256分辨率及10:1高宽比范围内的亚秒级推理。这些成果确立了OAT作为物理感知拓扑优化的通用、快速且分辨率无关的框架地位，同时提供的大规模数据集将推动逆向设计生成模型的深入研究。代码与数据详见：https://github.com/ahnobari/OptimizeAnyTopology。

English

Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design. Code & data can be found at https://github.com/ahnobari/OptimizeAnyTopology.

优化任意拓扑：一种形状与分辨率无关的结构拓扑优化基础模型

Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization

摘要

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