Machine Learning for Aerospace Systems Design and Optimization

Abstract

The aerospace industry has long been at the forefront of technological innovation, constantly pushing the boundaries of performance, efficiency, and reliability. The emergence of machine learning (ML) has introduced a new paradigm shift, transforming the way we design and optimize aerospace systems. This article explores the diverse applications of ML in aerospace, focusing on its impact on aerodynamic modeling, structural analysis, propulsion systems, control systems, and safety-critical systems. We review the state-of-the-art techniques, highlighting key success stories and future research directions. Additionally, we emphasize the challenges and opportunities associated with integrating ML into complex, multidisciplinary aerospace systems.