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

Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas Hogan, Darren McDonald

arXiv:2008.10740v1cs.LGeess.SPmath.OC

TL;DR

Aerospace engineering offers unusually strong opportunities for data-driven science because its workflows generate vast data and pose constrained, multi-objective optimization problems. This review synthesizes methods, challenges, and aerospace case studies into a roadmap, finding that integrated data management can potentially improve design optimization while safety-critical deployment remains bounded by certification, fidelity, and generalization requirements.

  • Problem

    Aerospace needs data-driven methods for data-intensive, constrained, multi-objective engineering problems, but safety-critical use requires certifiable, verifiable, interpretable, explainable, and generalizing models.

  • Method

    The review surveys data-driven aerospace engineering across design, manufacturing, testing, verification, validation, and services through technology discussions, case studies, and a future roadmap.

  • Results

    Integrated database management can potentially create a digital thread across design, manufacturing, and testing, with potential improvements to design optimization.

  • Takeaways & Limitations

    Data science is presented as complementing existing aerospace workflows while supporting predictive analytics, design insights, and digital-twin-based simulation.

  • Takeaways & Limitations

    Aerospace models must meet stringent reliability and certification requirements, while digital twins may lack sufficient fidelity for some precision-manufacturing processes.

Abstract

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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.

1 Introduction

Aerospace engineering is especially well positioned for data-driven methods because it combines abundant, multimodal data with constrained, multi-objective optimization. The review surveys opportunities across aerospace workflows while emphasizing certification, generalization, interpretability, explainability, and data-management challenges.

  • 1 Introduction: Aerospace engineering is data rich and already structured around constrained, multi-objective optimization suited to modern machine-learning methods.Manufacturing, testing, and service each generate extensive data, including multimodal production, sensor, and operational records.
  • 1 Introduction: Data science opportunities span factory operations, testing and evaluation, aircraft functions, and service activities.Examples include design for manufacturability, process control, inspection, certification, anomaly detection, and data-driven modeling.
  • 1 Introduction: Integrated data management could create a digital thread across design, manufacturing, and testing, while digital twins support efficient scenario simulation.The paper also connects data-intensive analysis with advances in fluid mechanics and materials science.
  • 1 Introduction: Safety-critical aerospace applications require data-driven models that are certifiable, verifiable, generalize beyond training data, interpretable, and explainable.The paper also warns that collecting and maintaining vast datasets can create a costly data mortgage.
  • 1 Introduction: The review combines a retrospective, current-state assessment, case studies, and a future roadmap for data-driven aerospace engineering.It also seeks common terminology, taxonomy, and hierarchy while distinguishing easy from difficult opportunities.

2 Machine Learning and Optimization

The paper introduces mathematical architectures for data-intensive aerospace-system modeling, then surveys optimization, physics-aware learning, and scalable numerical algorithms at a high level.

  • 2 Machine Learning and Optimization: This section presents mathematical architectures central to data-intensive modeling of aerospace systems.It covers machine learning, applied optimization, physics-governed systems, and scalable, robust numerical algorithms.

2.1 Machine learning

Machine learning builds data-based models through supervised, unsupervised, and reinforcement-learning paradigms, with deep networks and physics-informed or physics-guided methods extending these capabilities.

  • 2.1 Machine learning: Supervised learning maps labeled inputs x to outputs y, supporting classification for discrete labels and regression for continuous labels.Training commonly minimizes a loss such as root mean-squared error.
  • 2.1 Machine learning: Physics-informed learning incorporates known physical constraints through regularization, while physics-guided learning derives embeddings from first-principles model data.These approaches connect data-driven models to invariances, symmetries, conservation laws, and other physical structure.
  • 2.1 Machine learning: Machine-learning practice includes defining the objective, curating training data, selecting architecture and parameterization, and optimizing model parameters.Human judgment remains important throughout, and data collection and optimization can require substantial time and resources.
  • 2.1 Machine learning: Unsupervised learning finds structure without labels through clustering or continuous embeddings of high-dimensional data.Embedding methods can use encoder-decoder functions, with linear versions recovering SVD or PCA and nonlinear versions producing autoencoders.
  • 2.1 Machine learning: Reinforcement learning optimizes policies through environment interaction and immediate or delayed rewards, making it relevant to autonomy and control.Value functions quantify the desirability of states under a policy and discount rate γ.
  • 2.1 Machine learning: Deep learning uses multilayer neural networks as expressive architectures for supervised and reinforcement-learning models.Feed-forward, autoencoder, convolutional, and recurrent architectures support mappings, dimensionality reduction, vision, speech, and text applications.

2.2 Optimization

Optimization provides the mathematical foundation for machine learning, spanning problem modeling, algorithm design, implementation, and theoretical guarantees across convex, nonconvex, smooth, and nonsmooth settings.

  • 2.2 Optimization: Mathematical optimization combines theoretical analysis, algorithm design and implementation, and modeling of real-world or physical descriptions.The modeling process produces optimization problems whose parameter minimization or maximization yields inference or learning machines.
  • 2.2 Optimization: Convex optimization offers fast, scalable generic techniques with performance guarantees because convex objectives have a single global minimum or maximum.Nonconvex problems lack this general structure and require different analysis and algorithms.
  • 2.2 Optimization: Stochastic optimization scales gradient descent to datasets with millions of datapoints by using randomly sampled subsets.The approach reduces the cost of standard gradient computations for large machine-learning training sets.
  • 2.2 Optimization: More specific problem assumptions can enable faster algorithms and stronger guarantees than broad convex-versus-nonconvex classifications.The piecewise linear-quadratic class is presented as one example within convex optimization.
  • 2.2 Optimization: Nonconvex nonsmooth optimization addresses nondifferentiable objectives and constrained parameters, with neural networks and sparsity motivating important applications.Proximity operators provide a recurring subroutine for many such algorithms and can have closed-form or provably fast implementations under moderate assumptions.
  • 2.2 Optimization: Smooth optimization methods rely on gradients, matrix-vector products, and equation solves as atomic operations.Newton, Gauss-Newton, and quasi-Newton methods can be decomposed into these operations.

2.3 Scalable and robust algorithms

Scalable data analysis addresses massive datasets through randomized algorithms, while robust dimensionality-reduction methods extract meaningful low-dimensional structure despite outliers and corruption.

  • High-dimensional engineering datasets often contain low-dimensional structure, enabling dimensionality reduction and sparse representations of dominant features.This structure can reduce the effective degrees of freedom that models must characterize.
  • Randomized algorithms reduce the computational burden of traditional matrix factorizations by constructing smaller matrices with comparable performance.The framework extends beyond SVD and PCA to pivoted QR, pivoted LU, CP tensor, and dynamic mode decompositions.
  • Robust PCA decomposes data into a low-rank matrix L and sparse corruption or outlier matrix S, improving feature robustness to corrupted measurements.The low-rank component is analyzed using SVD to identify coherent features.
  • Selecting the truncation rank r carefully preserves meaningful patterns while discarding noise associated with small singular values.The optimal singular-value truncation threshold of Gavish and Donoho is cited as one way to determine r.
  • RPCA supports applications including video surveillance, natural language processing, matrix completion, and face recognition.In video surveillance, background objects can occupy L while foreground objects occupy S.

3 Digital Twin and Enabling Technologies

The section presents digital twins, reduced-order models, sparse sensing, discrepancy modeling, and uncertainty quantification as technologies for efficient aerospace simulation and trustworthy data-driven engineering. Their usefulness depends on accurate virtual representations, efficient reduced computations, explicit treatment of model mismatch, and performance guarantees.

  • 3.1 Digital Twin and Enabling Technologies: Digital twins aim to bridge physical and virtual worlds by simulating, testing, and evaluating designs more cheaply than real-world implementation.They rely on physics-based emulators and must integrate multiphysics components across entire engineering processes.
  • 3.1 Digital Twin and Enabling Technologies: Accurate digital twins remain challenging because current systems may lack sufficient fidelity and must ensure end-to-end performance across multiphysics processes.Discrepancy models provide an adaptive framework for updating physics models from sensor data.
  • 3.2 Sensor technology and the internet-of-things: Aerospace data volumes require edge computing and automated real-time feature extraction to shift from big data toward smart data.Sensor placement is also identified as important for improving efficiency and process control.
  • 3.2 Sensor technology and the internet-of-things: A high-dimensional state x can be approximated in a low-dimensional basis Φ_r as x ≈ Φ_ra, allowing sparse measurements to estimate the full state.The observation model y = Cx = CΦ_ra uses selected point measurements to recover the reduced coefficients.
  • 3.2 Sensor technology and the internet-of-things: Optimizing sensor locations improves reconstruction accuracy, but exhaustive sensor-location search is computationally intractable for moderately large n and r.Convex relaxations and greedy pivoted QR-based matrix-volume maximization are proposed alternatives.
  • 3.3 Reduced-order modeling: Reduced-order models replace n-dimensional systems with r-dimensional systems where r ≪ n, making previously intractable computations possible.Efficient nonlinear-term evaluation is necessary; sparse sampling methods such as gappy POD can help avoid returning to the original dimension n.
  • 3.4 Discrepancy modeling: Discrepancy modeling addresses measurement noise, disturbances, parameter errors, structural mismatch, incomplete measurements, delays, and latent variables in first-principles models.The discrepancy is the difference between model output and observed quantity of interest and can be modeled with Gaussian processes, dynamic mode decomposition, or model discovery.
  • 3.5 Uncertainty quantification: Uncertainty quantification provides probabilistic credibility and predictive-performance bounds needed to assess certification and reduction to practice for data-driven aerospace models.Without quantified uncertainty, robustness and sensitivity to observation error and model mismatch cannot be estimated.

4 Aerospace Design

Aircraft design has evolved into a complex, lifecycle-wide, constrained multi-objective optimization problem spanning many disciplines and requirements. Data-driven models, digital engineering, and physics-informed machine learning are presented as opportunities to support this complexity.

  • 4 Aerospace Design: Modern aircraft design spans refinement, testing, manufacturability, operations, maintenance, and disposal across many competing objectives and constraints.These include safety, environmental impact, ergonomics, range, fuel capacity, manufacturing costs, and product cash flow.
  • 4 Aerospace Design: Aircraft design is a constrained, multi-objective optimization problem with both discrete and continuous design parameters.Multidisciplinary design optimization connects objectives and constraints across aerodynamics, structures, propulsion, controls, cost, performance, and environmental impact.
  • 4 Aerospace Design: Composite fabrication makes manufacturability a central MDO discipline because final structures depend on geometry, material, and fabrication method.Reliable models must describe structure formation across processes such as hand lay-up, automated fiber placement, and vacuum forming.
  • 4 Aerospace Design: Physics-informed machine learning may raise first-principles fabrication models to the accuracy and efficiency required for multidisciplinary design optimization.The stated opportunity concerns modeling fabrication processes while retaining knowledge of the physical processes governing the final structure.
  • 4 Aerospace Design: Model-based engineering uses digital models and simulation across product design, production, maintenance, and support throughout the lifecycle.Data science contributes both to modeling physical asset behavior and to standardizing digital model formats for accessibility across engineering groups.
  • 4 Aerospace Design: Digital twins connect operational sensor data to physical aircraft systems containing many subsystems, components, signals, and communication channels.The passage frames identifying appropriate data and models as a challenge for real-time aircraft operations.

5 Aerospace Manufacturing

Aerospace manufacturing generates complex multimodal data across coordinated processes, creating opportunities for machine learning in quality control, standardization, assembly, materials, and inspection. The section also describes data-driven approaches for process monitoring, predictive shimming, materials testing, and composite forming.

  • 5 Aerospace Manufacturing: Modern aerospace manufacturing coordinates precisely timed stages while generating multimodal data from reports, metrology, video, supply chains, catalogues, and inspection.Priority applications include standardization, robotics, assembly, supply chain management, material fabrication, and non-destructive inspection.
  • 5.1 Advanced product quality planning: Machine learning may improve statistical process control by standardizing data formats and transferring prior process-control efforts across platforms and processes.Sensor-based models can also predict when production will move out of specification before human measurement and detection.
  • 5.2 Standardization: Data mining can identify similarities in prior aircraft designs to reduce airplane-specific parts and streamline future design decisions.The motivation is to address inefficiencies affecting supply chains, maintenance, and the assembly critical path.
  • 5.3 Automation and robotics: Automation can improve quality, production cycle time, cost, and repetitive-injury risk, but aircraft-specific requirements and tight tolerances complicate automation decisions.Successful automated systems require understanding the process and its requirements before control and machine-learning methods are applied.
  • 5.4 Assembly: Sparse sensing successfully supported predictive shimming for new wing-to-body joins using structural features mined from historical build data.The approach bypasses the planning and processing burden of high-fidelity metrology while supporting diagnostics and defect analysis.
  • 5.5 Materials: Physics-based models combined with data analytics and machine learning may reduce physical testing and accelerate materials screening and parameter exploration.The review describes correlating larger-scale behavior with fundamental properties and using physics-guided models for new material parameters.
  • 5.6 Composite fabrication: Composite parts are often formed from flat laminates into contoured profiles, where local compression, material variation, and manual work create wrinkle variability.The section identifies forming-process modeling, automated wrinkle detection, and vision-based analysis of manual actions as machine-learning opportunities.

6 Aerospace Verification and Validation

Aircraft verification and validation generate vast, heterogeneous data while requiring exceptionally reliable evidence for safety and certification. Digitized workflows and machine learning could automate analysis, improve test planning, and support real-time diagnosis, but must preserve data integrity, traceability, and confidence.

  • Testing landscape: Flight testing verifies performance and validates compliance, but a single test can generate more than 200,000 asynchronous multimodal sensor measurements.These data span digital and analog sensors, diverse physical quantities, and sampling rates from below 1 Hz to above 65 kHz.
  • Testing landscape: Aircraft complexity and failure-rate targets below 10^-9 or 10^-12 make rare failure examples a central machine-learning challenge.The Boeing 787 has approximately 2.3 million parts, 70 miles of wire, and 18 million avionics and flight-control source-code lines.
  • Digitization: Current verification workflows rely on engineers manually synthesizing upwards of 20 data sources, while systematic cross-flight and cross-program analysis is virtually absent.Data digitization is presented as an opportunity for pattern extraction and anomaly detection.
  • Digitization: Machine-learning-enabled digitization would automate laborious, reactive, discrete, and manual tasks while making process information available from concept through final test reporting.A centralized, real-time repository would support collaboration and engagement with test data as it is generated.
  • Challenges: Digitization must improve efficiency without compromising information integrity, accessibility, quality assessment, reliability, or traceability.The section identifies these requirements as significant challenges even while emphasizing the potential benefits of machine learning and uncertainty quantification.
  • Future testing: Digital-twin models and active learning could identify safety-critical test conditions and reduce the physical queries needed to validate models.Future testing would use principled, data-informed test selection, with physical tests increasing confidence in the model.
  • Future capabilities: Full-data capture and machine learning could support holistic analysis of high-dimensional, multimodal flight-test data, sensor fusion, real-time diagnosis, and adaptive flight-plan updates.Sparse sensor optimization is described as a way to reduce computational burden and enable real-time decisions rather than post-mortem analysis.

7 Aerospace Services

Aerospace service operations span many coordinated airport, maintenance, and fleet-support tasks with substantial costs from damage, delays, injuries, and unscheduled maintenance. Digital-thread and digital-twin models could connect service data with earlier lifecycle stages and improve maintenance and operational support.

  • Service opportunities: In-service operations offer opportunities to use data to improve maintenance and airside support from aircraft delivery through long-term fleet operation.Major customer expenses include aircraft damage, injury, airside-support delays, and unscheduled maintenance.
  • Ground service: Airport turnaround requires coordinated positioning, loading and unloading, fluid servicing, issue investigation, and visual pre-inspection.These tasks occur immediately upon arrival and prepare the aircraft for departure.
  • Maintenance: Unscheduled maintenance causes costly delays and schedule disruption, motivating systems that identify part anomalies and diagnose wear, low fluids, and software glitches.Customers must balance preventative-maintenance costs and time against the costs of unscheduled maintenance.
  • Lifecycle integration: A digital thread linking design, manufacturing, testing, and service data would allow information from each lifecycle stage to improve the others.The paper describes this as a reciprocal connection rather than a one-way handoff.
  • Digital twins: Digital twins incorporating aging and degradation models could improve fleet maintenance, sensor filtering, data assimilation, and anomaly diagnosis.The models would also support adjustments to design and manufacturing procedures for future aircraft.

8 Case Study: Predictive Assembly and Shimming

Predictive shimming uses historical production data, dimensionality reduction, and sparse sensing to estimate aircraft assembly gaps with far fewer measurements. The case study reports high tolerance compliance and potential efficiency gains in a time-critical assembly process.

  • Motivation: Shimming corrects assembly gaps caused by accumulated manufacturing tolerances and is among the most time-consuming and expensive part-to-part assembly activities.Aircraft components may be manufactured to specification yet still leave significant gaps when assembled into large structures.
  • Method: The proposed strategy learns gap distributions from historical data and designs optimized sparse sensing strategies to reduce data collection and processing.It assumes recurring patterns exist across aircraft that can be mined for future measurement design.
  • Method: Robust principal component analysis extracts coherent low-dimensional patterns from high-dimensional metrology data before sparse sensor optimization identifies informative measurement locations.The approach is intended for laser scans or point-cloud measurements and accounts for sparse outliers that corrupt coherent features.
  • Experiment: The PIXI-DUST demonstration used 10,076 laser gap measurements from 54 production instances of one Boeing aircraft type.Aligned measurement locations made the data suitable for constructing a data matrix and applying low-order modeling.
  • Results: 97-99% of gaps were predicted within the desired 0.005 inch machining tolerance, with optimal measurement rates ranging from 2% to 6% of shim points.The varying rates indicate that some shim regions require more features and sensors than others.
  • Results: Using around 3% of the original laser-scan measurements, the algorithm predicted most gap values within tolerance and achieved nearly twice the performance of random sensors.The optimized measurements showed strong cross-validated performance and could support targeted localized laser scans.

9 Case Study: V-22 Osprey

The V-22 Osprey case study frames digital twins and data science as responses to a program marked by engineering complexity, delays, cost overruns, and safety mishaps. A continuously updated, multi-physics digital twin could connect testing, simulation, and operating-limit decisions.

  • Program context: The V-22 combines helicopter-like takeoff and landing with fixed-wing range and speed, creating challenges in tiltrotor mechanics, aerodynamics, and control systems.Its development and testing were accompanied by delays, cost overruns, and safety mishaps.
  • Testing needs: A 2001 review panel called for extended high-rate-of-descent testing to define the high-risk flight-envelope region under appropriate conditions.The panel also recommended using those results to update operating limitations and the crew-training flight simulator.
  • Digital twin: A digital twin bridges physical and virtual systems by continuously updating internal models with real-world data and integrating multiple physics models across a system.Continuous learning requires the model to be tested, updated, and tested again.

10 Case Study: Urban Air Mobility

Urban air mobility aims to provide on-demand, point-to-point transportation in dense urban environments, but viable systems must address tightly coupled vehicle, infrastructure, monitoring, autonomy, and regulatory challenges.

  • 10 Case Study: Urban Air Mobility: Urban air mobility seeks on-demand, personal point-to-point transportation through obstacle-rich urban airspace.Its expected benefits include reduced congestion and travel times, lower pollution through electric aircraft, and broader technological benefits.
  • 10 Case Study: Urban Air Mobility: Vehicle design, testing, certification, fleet health monitoring, vertiports, maintenance, and logistics must be addressed together.The section identifies these as interconnected requirements for achieving the transportation concept.
  • 10 Case Study: Urban Air Mobility: Fixed-wing aircraft face runway constraints in cities, while helicopters present scaling, maneuverability, safety, and gliding limitations.These constraints motivate exploration of innovative electric vehicle configurations.
  • 10 Case Study: Urban Air Mobility: Computational fluid dynamics, finite element analysis, and data analytics can accelerate exploration of the rich vehicle design space.These tools simulate performance across flight and loading conditions, while additional analytics could reduce the design burden.
  • 10 Case Study: Urban Air Mobility: Future urban airspace may combine many vehicle types and piloting modes, increasing the need for autonomy, routing, deconfliction, and obstacle avoidance.The passage states that current autonomous capabilities are not yet mature enough for fielding and that policy adoption also remains incomplete.

11 Outlook

The outlook presents aerospace as a data-rich field with substantial opportunities for machine learning across design, manufacturing, testing, and services. Realizing these opportunities requires integrated data infrastructure, aerospace-specific and physics-informed algorithms, and regulatory approaches compatible with data-driven systems.

  • 11 Outlook: The review identifies high-priority data-science opportunities across aerospace design, manufacturing, testing, and services.Examples include new materials, streamlined flight testing, predictive assembly, non-destructive inspection, and digital-twin applications.
  • 11 Outlook: Digital twins can fuse multi-physics and multi-fidelity models with real-world data streams to improve design and testing cycles.The models are described as continuously improving within an integrated digital thread spanning critical processes and their interactions.
  • 11 Outlook: Data science and machine learning are expected to become core competencies for the future aerospace workforce.The paper links this outlook to the need for robust education in data-driven science and engineering.
  • 11 Outlook: Aerospace programs are increasingly enabled by extensive data, with current programs offering near-term opportunities and future programs enabling deeper integration.The paper distinguishes immediate benefits from existing data from the longer-term realization of the field’s full potential.
  • 11 Outlook: Implementing this vision requires integrated efforts from information technology, aerospace engineering, and data-science teams.Legacy storage systems, security, access, analytics integration, and the risk of collecting more data than can be analyzed are central architecture challenges.
  • 11 Outlook: Machine-learning models must be validated within their domains, incorporate physics, and support reliable certification for aerospace use.The paper emphasizes that models depend on training-data quality, are generally interpolative, and may not map directly onto existing compliance requirements.
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