mlsanityData to 7 Oct 2026

Improving aircraft performance using machine learning: a review

arXiv 2210.11481 ↗First posted 20 Oct 2022cs.LG physics.data-an physics.flu-dyncited by 19

Not on the map. Why?

Abstract (arXiv)

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines and provide our view on future opportunities. The basic concepts and the most relevant strategies for ML are presented together with the most relevant applications in aerospace engineering, revealing that ML is improving aircraft performance and that these techniques will have a large impact in the near future.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 39 of its 383 references that are in the map. First posted on arXiv 20 Oct 2022.

Builds on

39 of 383 references.

References · 39

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  4. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  5. 2021Machine learning accelerated computational fluid dynamicscited by 461
  6. 2019Machine Learning for Fluid Mechanicscited by 380
  7. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 335
  8. 2018Understanding disentangling in β-VAEcited by 332
  9. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 149
  10. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  11. 2016Information Dropout: Learning Optimal Representations Through Noisy Computationcited by 131
  12. 2018Discovering physical concepts with neural networkscited by 117
27 more
  1. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  2. 2021Physics-informed neural networks for solving Reynolds-averaged Navierunicodex2013Stokes equationscited by 68
  3. 2014Understanding Random Forests: From Theory to Practicecited by 57
  4. 2018Deep Neural Networks Learn Non-Smooth Functions Effectivelycited by 57
  5. 2021Scientific multi-agent reinforcement learning for wall-models of turbulent flowscited by 52
  6. 2020Convolutional-network models to predict wall-bounded turbulence from wall quantitiescited by 46
  7. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 44
  8. 2020Data-driven reduced-order models via regularized operator inference for a single-injector combustion processcited by 42
  9. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  10. 2019Machine learning in acoustics: theory and applicationscited by 36
  11. 2018Deep Neural Networks for Data-Driven Turbulence Modelscited by 35
  12. 2020Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Networkcited by 27
  13. 2020Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learningcited by 27
  14. 2020ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solverscited by 26
  15. 2022Physics-informed deep-learning applications to experimental fluid mechanicscited by 21
  16. 2017DR-RNN: A deep residual recurrent neural network for model reductioncited by 20
  17. 2020The role of surrogate models in the development of digital twins of dynamic systemscited by 20
  18. 2021Interpretable deep-learning models to help achieve the Sustainable Development Goalscited by 18
  19. 2022Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulationscited by 16
  20. 2021Grey-box models for wave loading predictioncited by 13
  21. 2022Predicting the temporal dynamics of turbulent channels through deep learningcited by 10
  22. 2022Physics-informed machine learning for Structural Health Monitoringcited by 8
  23. 2020Data-assisted combustion simulations with dynamic submodel assignment using random forestscited by 7
  24. 2020Learning Acoustic Scattering Fields for Dynamic Interactive Sound Propagationcited by 5
  25. 20213D Convolutional Selective Autoencoder For Instability Detection in Combustion Systemscited by 5
  26. 2021Predicting the near-wall region of turbulence through convolutional neural networkscited by 4
  27. 2022Local manifold learning and its link to domain-based physics knowledgecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 5; 2026: 2.

19 papers.

Sort:

By area:

  1. 2024Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectivecited by 8
  2. 2023A reinforcement learning strategy for p-adaptation in high order solverscited by 4
  3. 2024Towards certification: A complete statistical validation pipeline for supervised learning in industrycited by 4
  4. 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraintscited by 4
  5. 2025ONERA's CRM WBPN database for machine learning activities, related regression challenge and first resultscited by 4
  6. 2023A predictive physics-aware hybrid reduced order model for reacting flowscited by 3
  7. 2025A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structurescited by 3
  8. 2024Rapid aerodynamic prediction of swept wings via physics-embedded transfer learningcited by 2
  9. 2026FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshescited by 2
  10. 2023An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Datacited by 1
  11. 2024Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solverscited by 1
  12. 2025Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Losscited by 1
7 more
  1. 2025Reliable Statistical Guarantees for Conformal Predictors with Small Datasetscited by 1
  2. 2023An unsupervised machine-learning-based shock sensor for high-order supersonic flow solverscited by 0
  3. 2024Reinforcement learning to maximise wind turbine energy generationcited by 0
  4. 2024Aeroengine performance prediction using a physical-embedded data-driven methodcited by 0
  5. 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissionscited by 0
  6. 2024Reinforced Symbolic Learning with Logical Constraints for Predicting Turbine Blade Fatigue Lifecited by 0
  7. 2026Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNscited by 0