航空航天
认知重构
背景(考古学)
大数据
人工智能
计算机科学
制造工程
数据科学
工程管理
系统工程
机器学习
工程类
航空航天工程
数据挖掘
古生物学
生物
社会心理学
心理学
作者
Steven L. Brunton,J. Nathan Kutz,Krithika Manohar,Aleksandr Y. Aravkin,Kristi A. Morgansen,Jennifer Klemisch,Nicholas Goebel,James Buttrick,Jeffrey Poskin,Adriana W. Blom-Schieber,Thomas A. Hogan,D. C. McDonald
出处
期刊:AIAA Journal
[American Institute of Aeronautics and Astronautics]
日期:2021-07-20
卷期号:: 1-26
被引量:35
摘要
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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