A Comprehensive Review of the State of the Art in Generative Models for Aerodynamic Configuration Design of Aircraft

Generative design is transforming aerodynamic geometry optimization by enabling the exploration of design spaces beyond conventional direct or inverse paradigms. However, existing reviews seldom synthesize how generative priors integrate with physics-based fidelity and multidisciplinary constraints. This paper provides a comprehensive survey of recent advances in direct, inverse, and generative aerodynamic design. We categorize representative generative models—including conditional diffusion, operator learning, and multi-fidelity optimization—and highlight their coupling with physics-informed surrogates and feasibility control strategies. A unified four-stage pipeline is proposed to summarize the end-to-end process of generation, evaluation, optimization, and validation across aerodynamic applications. The survey further discusses challenges in scalability, data efficiency, and physical consistency, offering actionable recommendations for future research. By consolidating over one hundred recent studies, this review bridges the gap between generative machine learning and aerodynamic design practice, aiming to guide practitioners toward robust, physics-consistent generative frameworks.