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Cited by 270Advanced Aircraft Design and TechnologiesAerospace Engineering and ApplicationsAerospace and Aviation Technology

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

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Brunton, Steven L., Nathan Kutz, J., Manohar, Krithika, Aravkin, Aleksandr Y., Morgansen, Kristi, Klemisch, Jennifer, Goebel, Nicholas, Buttrick, James +4 more·2021·AIAA Journal
DOI 10.2514/1.j060131
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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.

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  • DL algorithms are particularly effective at processing complex data, such as images and video, and can be used to identify cargo types and detect anomalies, such as damaged or dangerous cargo.

    “DL algorithms are particularly effective at processing complex data, such as images and video, and can be used to identify cargo types and detect anomalies, such as damaged or dangerous cargo [114] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [114]

    Section 2.1.4 describes deep convolutional neural networks as widely used for machine vision, and Section 5.3 says machine-learning tools can detect faults and potential failures in sensor data. Neither passage connects these capabilities to identifying cargo types or detecting damaged or dangerous cargo. Page numbers were unavailable in our copy. (LLM, conf=0.87)

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Cited by

Sorted by citations

Authors
Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., Hogan, T., & McDonald, D.
Affiliations
  • University of Washington (US)
  • University of Washington
  • Boeing (United States) (US)
  • The Boeing Company
ORCID
Venue
AIAA Journal
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Published
2021-08
Volume
59
Issue
8
Pages
2820-2847
ISSN
  • 0001-1452 (ISSN-L)
  • 1533-385X
DOI
10.2514/1.j060131
OpenAlex
W3081456333
Cited by
270
References
10
Citations by year
2026: 69 · 2025: 69 · 2024: 62 · 2023: 36 · 2022: 23 · 2021: 11
FWCI
13.04
Type
journal article
Language
en
Open access
Locations
  • AIAA Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Boeing (2018-ETT-PA-379)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Advanced Aircraft Design and Technologies
  • Physical Sciences › Engineering › Control and Systems Engineering › Aerospace Engineering and Applications
  • Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
Keywords
machine learning, aircraft design, aerospace manufacturing, constrained multi-objective optimization, explainable machine learning, safety-critical applications, verification and validation, big data, non-convex optimization

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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