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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| Veröffentlicht in: | Journal of mechanical science and technology Jg. 38; H. 8; S. 3899 - 3919 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Seoul
Korean Society of Mechanical Engineers
01.08.2024
Springer Nature B.V 대한기계학회 |
| Schlagworte: | |
| ISSN: | 1738-494X, 1976-3824 |
| Online-Zugang: | Volltext |
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