数据驱动的航空航天工程：用机器学习重塑行业
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
摘要：
数据科学，特别是机器学习，正在迅速改变科学和工业领域。航空航天业正准备利用大数据和机器学习，这两种技术擅长解决飞机设计和制造中出现的多目标、约束优化问题。事实上，机器学习中的新兴方法可以被认为是数据驱动的优化技术，其对于高维、非凸和受约束的多目标优化问题是理想的，并且随着数据量的增加而改进。在这篇综述中，我们将探讨将数据驱动的科学和工程整合到航空航天工业中的机遇和挑战。重要的是，我们将专注于安全关键应用程序对可解释，可推广，可解释和可认证的机器学习技术的迫切需求。本次审查将包括回顾、对当前最新技术水平的评估以及展望未来的路线图。最近的算法和技术趋势将在航空航天设计，制造，验证，确认和服务的关键挑战的背景下进行探讨。此外，我们将通过航空航天行业的几个案例研究来探索这一前景。本文件是华盛顿大学和波音公司密切合作的结果，总结了过去的努力，并概述了未来的机会。
英文摘要：
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, non-convex, and constrained, multi-objective optimization problems, and that improve with increasing volumes of data. In this review, we will explore the opportunities and challenges of integrating data-driven science and engineering into the aerospace industry. Importantly, we will focus on the critical need for interpretable, generalizeable, 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, we will explore this landscape through several case studies in the aerospace industry. This document is the result of close collaboration between UW and Boeing to summarize past efforts and outline future opportunities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
