AEROSPACE DESIGN BASED ON SEMANTIC VECTOR SEARCH AND TRANSFER LEARNING

This paper explores the potential of a decision support system for aerospace design that integrates semantic vector search and transfer learning.The study investigates the application of transfer learning to adapt pre-trained neural networks for aerospace-specific datasets and examines how semantic vector search can transform complex design data into high-dimensional vector representations to reveal latent relationships.Various supervised and unsupervised learning approaches are evaluated to address different phases of the aerospace lifecycle.Preliminary validation on both synthetic and real-world datasets indicates promising improvements in design analysis efficiency and predictive accuracy, laying a foundation for further development of AI-based decision support systems in aerospace engineering.