Multiple performance-driven solutions (wings) with high-level goals are generated by an infinite scale cloud computing solution executing a machine learning-based GD algorithm. This study investigates the potential applications of this Computer-Associated Design (CAsD) technology to generate novel micro aerial vehicle wing concepts that are structurally more stable and efficient. However, the novel Generative Design (GD) method claims to produce mechanically improved solutions based on robust and rigorous models of design conditions and performance criteria. Artificial wings, on the other hand, are limited to the human-proposed conceptual design phase often leading to sub-optimal results. Natural wings being fundamentally responsible for this phenomenon are developed over millions of years of evolution. Gliding is generally one of the most efficient modes of flight in natural fliers that can be further emphasized in the aircraft industry to reduce emissions and facilitate endured flights.
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