# Improving aircraft performance using machine learning: a review

arXiv: https://arxiv.org/abs/2210.11481 · HTML: https://mlsanity.com/paper/2210.11481/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 383 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 30 of 39)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Enhancing Computational Fluid Dynamics with Machine Learning](https://mlsanity.com/paper/2110.02085/) (2021)
- [Information Dropout: Learning Optimal Representations Through Noisy Computation](https://mlsanity.com/paper/1611.01353/) (2016)
- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Physics-informed neural networks for solving Reynolds-averaged Navierunicodex2013Stokes equations](https://mlsanity.com/paper/2107.10711/) (2021)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [Deep Neural Networks Learn Non-Smooth Functions Effectively](https://mlsanity.com/paper/1802.04474/) (2018)
- [Scientific multi-agent reinforcement learning for wall-models of turbulent flows](https://mlsanity.com/paper/2106.11144/) (2021)
- [Convolutional-network models to predict wall-bounded turbulence from wall quantities](https://mlsanity.com/paper/2006.12483/) (2020)
- [Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows](https://mlsanity.com/paper/2109.01514/) (2021)
- [Data-driven reduced-order models via regularized operator inference for a single-injector combustion process](https://mlsanity.com/paper/2008.02862/) (2020)
- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Deep Neural Networks for Data-Driven Turbulence Models](https://mlsanity.com/paper/1806.04482/) (2018)
- [Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network](https://mlsanity.com/paper/2002.09062/) (2020)
- [Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning](https://mlsanity.com/paper/2008.10740/) (2020)
- [ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers](https://mlsanity.com/paper/2101.04749/) (2020)
- [The role of surrogate models in the development of digital twins of dynamic systems](https://mlsanity.com/paper/2001.09292/) (2020)
- [Physics-informed deep-learning applications to experimental fluid mechanics](https://mlsanity.com/paper/2203.15402/) (2022)
- [DR-RNN: A deep residual recurrent neural network for model reduction](https://mlsanity.com/paper/1709.00939/) (2017)
- [Interpretable deep-learning models to help achieve the Sustainable Development Goals](https://mlsanity.com/paper/2108.10744/) (2021)

## Built on by (most cited first, 19 of 19)

- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [A reinforcement learning strategy for p-adaptation in high order solvers](https://mlsanity.com/paper/2306.08292/) (2023)
- [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)
- [ONERA's CRM WBPN database for machine learning activities, related regression challenge and first results](https://mlsanity.com/paper/2505.06265/) (2025)
- [A predictive physics-aware hybrid reduced order model for reacting flows](https://mlsanity.com/paper/2301.09860/) (2023)
- [A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures](https://mlsanity.com/paper/2509.10227/) (2025)
- [FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes](https://mlsanity.com/paper/2604.08586/) (2026)
- [Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning](https://mlsanity.com/paper/2409.12711/) (2024)
- [An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data](https://mlsanity.com/paper/2401.08663/) (2023)
- [Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers](https://mlsanity.com/paper/2407.19000/) (2024)
- [Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss](https://mlsanity.com/paper/2507.22428/) (2025)
- [Reliable Statistical Guarantees for Conformal Predictors with Small Datasets](https://mlsanity.com/paper/2512.04566/) (2025)
- [An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers](https://mlsanity.com/paper/2308.00086/) (2023)
- [Reinforcement learning to maximise wind turbine energy generation](https://mlsanity.com/paper/2402.11384/) (2024)
- [Aeroengine performance prediction using a physical-embedded data-driven method](https://mlsanity.com/paper/2407.00501/) (2024)
- [Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions](https://mlsanity.com/paper/2407.13320/) (2024)
- [Reinforced Symbolic Learning with Logical Constraints for Predicting Turbine Blade Fatigue Life](https://mlsanity.com/paper/2412.03580/) (2024)
- [Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs](https://mlsanity.com/paper/2606.27126/) (2026)

## 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.