Artificial Intelligence in Aerospace Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".
Deadline for manuscript submissions: 25 January 2026 | Viewed by 114
Special Issue Editors
2. AEROG, Universidade da Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
Interests: aerospace; management; safety; planetary defense; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
2. AEROG-LAETA, Universidade da Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
Interests: aerodynamics and propulsion; two-phase flows; combustion; planetary defense; space flight mechanics; spacecraft attitude motion; satellites and spacecraft passive orientation systems
Special Issues, Collections and Topics in MDPI journals
2. LAETA/AEROG, University of Beira Interior, 6201-001 Covilhã, Portugal
Interests: power systems management and operation; power electronics and its applications; renewable energies; smart grids; cryogenic systems; avionics systems; aeronautics and space systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Artificial Intelligence (AI) in aerospace is ushering in a paradigm shift, redefining traditional boundaries in every industry. This Special Issue will explore the full spectrum of AI applications across the aerospace sector, ranging from intelligent flight systems and autonomous aerial vehicles to AI-driven data processing and predictive maintenance.
AI techniques, including machine learning, deep learning, reinforcement learning, natural language processing, and computer vision, are increasingly employed to optimize complex systems, reduce operational costs, enhance mission reliability, and enable real-time decision-making in high-stakes environments. This convergence of AI with aerospace engineering not only accelerates innovation in existing domains but also enables disruptive advances across emerging and interdisciplinary areas, with particular relevance to predictive maintenance and lifecycle optimization.
This Special Issue welcomes original research articles, comprehensive reviews, and visionary perspectives that demonstrate novel AI methods, system-level applications, interdisciplinary integrations, and real-world deployments in aerospace contexts. Contributions should ideally reflect both theoretical advancements and practical implementations, emphasizing scalability, robustness, and ethical considerations.
Dr. Luís Santos
Dr. André Silva
Dr. Rui Melicio
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Applied Sciences is an international peer-reviewed open access semimonthly 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
- artificial intelligence
- machine learning
- deep learning
- neural networks
- AI vision
- aerospace
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