Aircraft design optimization is essential for improving aircraft performance (such as reduced fuel consumption and lowered noise), which leads to more efficient, sustainable, and affordable aircraft. Conventional aircraft design adopts physics-based simulation models, but iteratively evaluating simulation models is computationally intensive, or even practically impossible. Meanwhile, artificial intelligence (AI) emerges as a revolutionary game changer in the modern engineering industry, including aircraft design optimization. Generative AI (genAI), one of the groundbreaking AI methods, has been advancing aircraft design optimization from various aspects, including intelligent parameterization, predictive modeling, training facilitation, and constraints handling. However, there is a lack of a review summarizing genAI applications in aircraft design optimization. This paper encapsulates four key genAI methods (namely, variational autoencoder, generative adversarial networks, diffusion, and transformer models), followed by advantages and drawbacks, as well as crucial advancements in aircraft design. This work aims to synthesize existing knowledge, identify research gaps, and guide future research for the genAI and aircraft design optimization communities.
Xiaosong Du (Sun,) studied this question.