Research progress and prospects of aircraft aerodynamic design based on generative models

As one of the fastest-growing directions in deep learning, the generative model has achieved remarkable success in realms such as computer vision and has also introduced novel paradigms and methodologies for research endeavors within the scientific fields like aerodynamics. This paper focuses on the latest research advancements of generative models in the field of aircraft aerodynamic configuration design, and systematically summarizes the relevant research achievements in recent years. Firstly, a representation-generation-evaluation framework for generative aerodynamic configuration design of aircraft is established. Subsequently, the key technologies and current development progress involved in aerodynamic configuration design are examined and discussed from the perspectives of aerodynamic configuration representation, the development of generative aerodynamic configuration design models, and methods for evaluating design quality. Additionally, a brief overview of aerodynamic data construction methods and typical datasets is provided, serving as a data foundation for generative aerodynamic design. Lastly, the future key development directions in the field of generative aerodynamic configuration design are discussed, including exploration of hybrid generative model architectures, construction of large models and domain-specific agents for aerodynamic design, establishment of a comprehensive evaluation system for generative aerodynamic design quality, and integration of domain knowledge into generative aerodynamic design models.