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

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.

Uncovering near-wall blood flow from sparse data with physics-informed neural networks

“…What makes such purely datadriven approaches for estimating hemodynamics from morphology and boundary conditions [49,50] even more challenging is the high dimensionality of parameters of interest (e.g., WSS), which are typically vectorial quantities with spatiotemporal variation. Therefore, the notion of "smart data" compared to "large data" [51] is expected to be the new frontier in hemodynamics machine learning research. Sparsity based models have high potential in hemodynamics modeling (reviewed in [13]) and the present work demonstrates the power of physics-informed machine learning in leveraging sparse data for near-wall blood flow modeling.…”

“…Digital twins often rely on a continuous feed of data. However, obtaining real-time large datasets and processing them in real-time represents a major challenge [51]. Physics-informed machine learning models could eliminate the need for large datasets and therefore are expected to be a key component in future digital twin models.…”