Engineering complex systems (aircraft, buildings, vehicles) requires coordinating geometric and performance couplings across subsystems. As generative models proliferate for specialized domains, a key research gap is how to coordinate frozen, pre-trained submodels to generate full-system designs that are feasible, diverse, and high-performing. We introduce GLUE, which orchestrates pre-trained, frozen generators while enforcing system-level feasibility, optimality, and diversity. Compatible models must be end-to-end differentiable with a smooth, well-behaved latent-to-output mapping. We propose and benchmark (i) data-driven GLUE models trained on pre-generated system-level designs and (ii) a data-free GLUE model trained on a differentiable geometry layer. On a UAV design problem with five coupling constraints, we find that data-driven approaches yield diverse, high-performing designs but require large datasets to satisfy constraints reliably. The data-free approach is competitive with Bayesian optimization and gradient-based optimization in performance and feasibility while training a full generative model in only ~10 min on an RTX 4090 GPU, requiring more than two orders of magnitude fewer geometry evaluations and FLOPs than the data-driven method. We identify equality constraint satisfaction as a key difficulty and remaining limitation, and ablate approaches that improve this for the data-free approach. As a first step toward scaling generative design to complex, real-world engineering systems, this work explores how unmodified, domain-informed submodels can be integrated into a modular generative workflow.

翻译：工程复杂系统（飞机、建筑、车辆）的设计需要协调各子系统间的几何与性能耦合。随着专业领域生成模型的广泛应用，一个关键研究空白是如何协调冻结的预训练子模型，生成可行、多样且高性能的全系统设计方案。本文提出GLUE框架，该框架能编排冻结的预训练生成器，同时确保系统级可行性、最优性与多样性。兼容模型需具备端到端可微性，并具有平滑、行为良好的隐空间至输出映射。我们提出并对比了：(i) 基于预生成系统级设计训练的数据驱动型GLUE模型，以及(ii) 基于可微几何层训练的无数据型GLUE模型。在具有五个耦合约束的无人机设计问题中，数据驱动方法能生成多样且高性能的设计方案，但需大规模数据集才能可靠满足约束条件。无数据方法在性能与可行性方面可与贝叶斯优化和基于梯度的优化相媲美，且在RTX 4090 GPU上仅需约10分钟即可完成完整生成模型的训练，其所需的几何评估次数与浮点运算量比数据驱动方法低两个数量级以上。我们确定等式约束满足是核心难点与现有局限，并针对无数据方法进行了消融实验以改进该问题。作为向真实工程复杂系统扩展生成式设计的初步探索，本研究揭示了如何将未经修改的领域知识子模型集成至模块化生成工作流中。