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The study presents complexity-driven layout exploration for aircraft structures (CD-LEAS), a novel process for efficient topology optimization. Case studies confirm CD-LEAS’s ability to produce simple, light, stiff, and buckling-resistant layouts.

This research leverages a deep learning model to improve scalability in topology optimization, with a new approach that reduces computation time by 36.84% and maintains mechanical performance.