Active Learning Enhanced Neural Networks for Aerodynamics Design in Military and Civil Aviation

Abstract The use of adaptive neural networks in aerodynamics design has become one of the most promising recent invention in both military and civil aircraft design, providing new approaches to the solution of a number of problematic issues connected with optimization of aircraft performance. Herein, this review provides a synthesis of neural networks and aerodynamics by emphasizing their ability to facilitate advanced design engineering, expedite the design process, as well as promote the usability and effectiveness of higher performing systems. Neural networks are involved in shape optimization, drag cutting, real time aircraft modifications and other key issue areas attesting to their capability in handling aerodynamics. Employing methods like supervised learning, reinforcement learning, and physics aware neural networks these networks can simulate non-linear multidimensional systems and arrive at solutions that are impossible through ordinary methods. The usage of these tools has been pushed even more over time, due to new advancements such as High-Performance Computing and specialized hardware. The review also considers effective application of systematic adaptive neural networks in the military and civil aviation hypersonic vehicle design, stealth aircraft design and optimization, the new fuel-efficient wings, and flight efficiency systems for real time control. The results put into evidence benefits of neural networks for cutting down design cycles, boosting MPG, increasing safety, and encouraging environmentally friendly solutions. The future for aerospace engineering will be in the hands of adaptive neural networks as part of the development of the aviation industry, dictating new advancements in both military and commercial aviation.