Neural network design for data-driven prediction of target geometry for an aerodynamic inverse design algorithm

With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness....

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Vydané v:	Journal of mechanical science and technology Ročník 38; číslo 8; s. 3899 - 3919
Hlavní autori:	Shirvani, Ahmad, Nili-Ahmadabadi, Mahdi, Ha, Man Yeong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Seoul Korean Society of Mechanical Engineers 01.08.2024 Springer Nature B.V 대한기계학회
Predmet:	Aerodynamics Algorithms Artificial intelligence Artificial neural networks Computational efficiency Control Deep learning Design parameters Dynamical Systems Engineering Geometry Impact prediction Industrial and Production Engineering Inverse design Iterative methods Machine learning Mechanical Engineering Multilayers Network design Neural networks Original Article Vibration 기계공학 Aerodynamic design Deep learning Neural network design Data-driven computational cost reduction Target prediction
ISSN:	1738-494X, 1976-3824
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