Generative adversarial networks enable biomimetic topology fusion with balanced mechanical performance and aesthetic quality

Conventional structural design studies often prioritize mechanical metrics, yet lack a unified narrative that renders the aesthetic expression of form both quantifiable and verifiable. To address this gap, we develop a GAN-based framework for biomimetic topology fusion generation, leveraging Cycle-Consistent GANs (CycleGAN) to learn bidirectional mappings and morphological translations between two classes of natural prototypes under unpaired supervision: performance-oriented morphologies (e.g., dragonfly wing venation and leaf venation), which exhibit high structural efficiency but comparatively weak visual order, and aesthetics-oriented patterns (e.g., honeycomb cells and pinecone spirals), which display pronounced geometric regularity and proportional structure but limited load-bearing capacity. Through cross-domain translation and fusion, the model synthesizes hybrid topological textures that simultaneously encode cues of structural robustness and ordered geometric features. These synthesized morphologies are subsequently validated via flexural (bending) testing in terms of load-carrying capacity and energy absorption efficiency, and are objectively characterized by a multi-metric aesthetic quantification scheme-computed on binary, vectorized structural maps-covering symmetry, complexity, and order. Across multiple morphology-pair settings, the fusion-generated structures exhibit a more balanced overall profile in both mechanical response and aesthetic metrics, indicating effective synergy between engineering usability and visual expression. In addition, we provide an application example in conceptual form design for orthopedic exoskeletal products, illustrating the cross-domain potential of the proposed approach at the interface of engineering design and aesthetic design.