The aerospace industry is poised to capitalize on Big Data and machine learning, which excels at solving the types of multi-objective, constrained optimization problems that arise in aircraft design and manufacturing. Indeed, emerging methods in machine learning may be thought of as data-driven optimization techniques that are ideal for high-dimensional and multi-objective optimization problems, improving with increasing volumes of data. This chapter will explore the opportunities and challenges of integrating data-driven science and engineering into the aerospace industry. This chapter will include a retrospective, an assessment of the current state-of-the-art, and a roadmap looking forward. Recent algorithmic and technological trends will be explored in the context of critical challenges in aerospace design,manufacturing, verification, validation, and services.

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