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

2021/07/20 by Steven L. Brunton, J. Nathan Kutz, Krithika Manohar +11 · 28 citations
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Aerospace #Aerospace engineering #Artificial intelligence #Big data #Cognitive reframing #Computer science #Context (archaeology) #Data mining #Data science #Engineering #Engineering management #Machine learning #Manufacturing engineering #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Systems engineering

paper · open access · doi:10.2514/1.j060131

published in AIAA Journal 59(8), 2820-2847 (American Institute of Aeronautics and Astronautics)

openalex publication_date 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. 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, nonconvex, and constrained, multi-objective optimization problems, and that improve with increasing volumes of data. This review will explore the opportunities and challenges of integrating data-driven science and engineering into the aerospace industry. Importantly, this paper will focus on the critical need for interpretable, generalizable, explainable, and certifiable machine learning techniques for safety-critical applications. This review 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. In addition, this review will explore this landscape through several case studies in the aerospace industry. This document is the result of close collaboration between University of Washington and Boeing to summarize past efforts and outline future opportunities.

Citations

• ImageNet classification with deep convolutional neural networks
• Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows

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