Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II)
A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 2969
Special Issue Editor
Interests: aerospace engineering; multidisciplinary optimization; machine learning; data science; artificial intelligence; space robotics; UAVs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Multidisciplinary Optimization (MDO) and Artificial Intelligence (AI)/Machine Learning (ML) play an increasingly important role in aerospace applications. All this is particularly true in space and modern aerospace engineering, where a variety of technological opportunities have arisen, each requiring novel approaches and algorithms to address the corresponding technological challenges. The aerospace industry presents several unique opportunities and challenges for the integration of data intensive analysis techniques. These methods hold the promise of bringing new possibilities to the development of new robotic platform designs, multisensory navigation and space exploration approaches to previously unsolvable problems. With the advancement in AI, robotics and unmanned aerial vehicle (UAV) technology, ML has emerged as a viable technology for solving constrained multi-objective optimization problems (CMOPs). Aided by advances in hardware and algorithms, modern ML is poised to enable this optimization, allowing a much broader and integrated perspective.
The goal of this Special Issue is to illustrate applications of Multidisciplinary Optimization, Machine Learning and Artificial Intelligence methods to problems in Aerospace Engineering. Novel ML/AI algorithms and/or application of existing approaches to problems involving space exploration, UAV operations, space robotics and other fields of aerospace engineering will be examined.
Topics of interest include but are not limited to:
- Machine Learning and Artificial Intelligence for Space and Aerospace Applications;
- Multi-objective Optimization for Space and Aerospace Applications;
- Autonomous Agents and Multiagent Systems in Aerospace Applications;
- UAV Trajectory Optimization Using Machine Learning;
- Energy-efficient UAV Communications;
- Robotics, Perception and Vision in Aerospace Applications;
- Intelligent Control for Space and Aerospace Systems;
- Big Data, Machine Learning and Data Mining in Aerospace Applications;
- Planning and Scheduling for Autonomous Systems;
- Data Processing and Satellite Applications;
- Bio-inspired Solutions for System Design and Control;
- AI for Air and Space Traffic Management and Operations.
Dr. Jules Simo
Guest Editor
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. 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
- aerospace engineering
- multidisciplinary optimization
- artificial intelligence
- machine learning
- data science
- UAVs
