Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning

Data science, and machine learning in particular, is rapidly transforming the scientific industrial landscapes. The aerospace industry poised to capitalize on big data learning, which excels at solving types of multi-objective, constrained optimization problems that arise aircraft design manufacturing. Indeed, emerging methods may be thought as data-driven techniques are ideal for high-dimensional, nonconvex, constrained, multi-objective problems, improve with increasing volumes data. This review will explore opportunities challenges integrating science engineering into industry. Importantly, this paper focus critical need interpretable, generalizable, explainable, certifiable safety-critical applications. include a retrospective, an assessment current state-of-the-art, roadmap looking forward. Recent algorithmic technological trends explored context design, manufacturing, verification, validation, services. In addition, landscape through several case studies document result close collaboration between University Washington Boeing summarize past efforts outline future opportunities.