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

TL;DR

This paper reviews how machine learning is revolutionizing aerospace engineering by enabling data-driven optimization in design and manufacturing, emphasizing interpretability, safety, and future research directions.

Contribution

It provides a comprehensive overview of current machine learning applications in aerospace, highlighting challenges, opportunities, and a collaborative roadmap for future integration.

Findings

Machine learning enhances multi-objective aerospace optimization.

Interpretable and certifiable ML methods are critical for safety.

Case studies demonstrate successful industry applications.

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, 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.