GLUE: Generative Latent Unification of Expertise-Informed Engineering Models

• URL: http://arxiv.org/abs/2512.19469v1
• Date: Mon, 22 Dec 2025 15:23:19 GMT
• Title: GLUE: Generative Latent Unification of Expertise-Informed Engineering Models
• Authors: Tim Aebersold, Soheyl Massoudi, Mark D. Fuge,
• Abstract summary: We introduce Generative Latent Unification of Expertise-Informed Engineering Models (GLUE)<n>GLUE orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity.<n>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.
• Score: 3.005158583027536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Engineering complex systems (aircraft, buildings, vehicles) requires accounting for geometric and performance couplings across subsystems. As generative models proliferate for specialized domains (wings, structures, engines), 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 Generative Latent Unification of Expertise-Informed Engineering Models (GLUE), which orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity. We propose and benchmark (i) data-driven GLUE models trained on pre-generated system-level designs and (ii) a data-free GLUE model trained online 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 a RTX 4090 GPU, requiring more than two orders of magnitude fewer geometry evaluations and FLOPs than the data-driven method. Ablations focused on data-free training show that subsystem output continuity affects coordination, and equality constraints can trigger mode collapse unless mitigated. By integrating unmodified, domain-informed submodels into a modular generative workflow, this work provides a viable path for scaling generative design to complex, real-world engineering systems.

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