O'rta Osiyo me'morchiligi elementlarini zamonaviy binolar 3D modellariga avtomatik integratsiya qilishning neyron tarmoq uslublarini takomillashtirish

The preservation of regional cultural heritage within contemporary urban infrastructure necessitates advanced computational approaches to architectural design. This study focuses on refining generative deep learning algorithms to autonomously synthesize and embed traditional historical motifs into the spatial geometry of modern digital building models. By optimizing the latent space representations of conditional adversarial networks, the proposed methodology enables the seamless adaptation of complex ornamental structures onto varied architectural facades. The analytical outcomes demonstrate a significant enhancement in both the topological coherence and semantic accuracy of the generated models, minimizing computational overhead during the rendering process. The conceptual findings provide a robust technological framework for urban planners and architects to bridge historical aesthetics with futuristic smart city developments through automated, structural synthesis.