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

arXiv: https://arxiv.org/abs/2008.10740 · HTML: https://mlsanity.com/paper/2008.10740/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 27 of 27)

- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems](https://mlsanity.com/paper/2306.13867/) (2023)
- [The transformative potential of machine learning for experiments in fluid mechanics](https://mlsanity.com/paper/2303.15832/) (2023)
- [Reduced Order Modeling with Shallow Recurrent Decoder Networks](https://mlsanity.com/paper/2502.10930/) (2025)
- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems](https://mlsanity.com/paper/2206.04811/) (2022)
- [PhySense: Sensor Placement Optimization for Accurate Physics Sensing](https://mlsanity.com/paper/2505.18190/) (2025)
- [Towards certification: A complete statistical validation pipeline for supervised learning in industry](https://mlsanity.com/paper/2411.02075/) (2024)
- [Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints](https://mlsanity.com/paper/2505.02634/) (2025)
- [Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering](https://mlsanity.com/paper/2307.04599/) (2023)
- [EngineBench: Flow Reconstruction in the Transparent Combustion Chamber III Optical Engine](https://mlsanity.com/paper/2406.03325/) (2024)
- [A discrete physics-informed training for projection-based reduced order models with neural networks](https://mlsanity.com/paper/2504.13875/) (2025)
- [Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence](https://mlsanity.com/paper/2309.10266/) (2023)
- [Into the Void: Mapping the Unseen Gaps in High Dimensional Data](https://mlsanity.com/paper/2501.15273/) (2025)
- [BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs](https://mlsanity.com/paper/2506.23024/) (2025)
- [Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2510.03604/) (2025)
- [Reliable Statistical Guarantees for Conformal Predictors with Small Datasets](https://mlsanity.com/paper/2512.04566/) (2025)
- [Uncertainty-Aware Data-Based Method for Fast and Reliable Shape Optimization](https://mlsanity.com/paper/2601.21956/) (2026)
- [Adjoint-based shape optimization of a ship hull using a Conditional Variational Autoencoder (CVAE) assisted propulsion surrogate model](https://mlsanity.com/paper/2602.14907/) (2026)
- [Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation](https://mlsanity.com/paper/2605.29911/) (2026)
- [A local approach to parameter space reduction for regression and classification tasks](https://mlsanity.com/paper/2107.10867/) (2021)
- [Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2305.16924/) (2023)
- [Prevention of Overfitting on Mesh-Structured Data Regressions with a Modified Laplace Operator](https://mlsanity.com/paper/2507.06631/) (2025)
- [The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations](https://mlsanity.com/paper/2507.08908/) (2025)
- [Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design](https://mlsanity.com/paper/2604.18062/) (2026)
- [AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation](https://mlsanity.com/paper/2605.20303/) (2026)
- [MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction](https://mlsanity.com/paper/2605.26718/) (2026)

## Abstract (arXiv)

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.