Machine Learning for Aerodynamic Analysis and Optimization

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 705

Special Issue Editors


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Guest Editor
Department of Aeronautics, Faculty of Engineering, Imperial College London, London, UK
Interests: machine learning technologies in aerodynamic studies and optimizations; adaptive sampling methods; surrogate model assisted optimization; reinforcement learning

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Guest Editor
Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO171BJ, UK
Interests: aerodynamics; structures; aeroelasticity; model reduction; control
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Special Issue Information

Dear Colleagues,

This Special Issue brings together cutting-edge research at the intersection of aerospace engineering and machine learning, with a focus on advancing aerodynamic analysis and optimization. While data-driven models have shown remarkable promise in reducing computational cost and accelerating design, their adoption in real-world aerospace applications depends on more than just accuracy. Physics-driven approaches are key to enhancing interpretability, generality, and reliability, ensuring that machine learning methods remain consistent with the fundamental laws of aerodynamics.

The contributions in this Special Issue highlight novel frameworks that integrate physics-driven approaches, enable robust predictions across diverse flow regimes, and extend learning-based methods from simplified cases to realistic three-dimensional aircraft configurations. Beyond prediction, we also emphasize the role of machine learning in guiding aerodynamic design and optimization, opening new pathways for innovation in aircraft performance, efficiency, and sustainability.

This Special Issue serves both as a resource for researchers developing new data-driven and physics-driven techniques and as a guide for practitioners seeking reliable, interpretable, and scalable tools for aerospace applications.

Dr. Runze Li
Dr. Andrea Da-Ronch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Dr. Runze Li
Dr. Andrea Da-Ronch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aerodynamics
  • machine learning
  • physics-driven
  • generality
  • interpretability
  • reliability

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Published Papers (1 paper)

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Research

19 pages, 5020 KB  
Article
Mesh-Agnostic Model for the Prediction of Transonic Flow Field of Supercritical Airfoils
by Runze Li, Yue Fu, Yufei Zhang and Haixin Chen
Aerospace 2026, 13(2), 117; https://doi.org/10.3390/aerospace13020117 - 24 Jan 2026
Viewed by 460
Abstract
Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic [...] Read more.
Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article
(This article belongs to the Special Issue Machine Learning for Aerodynamic Analysis and Optimization)
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