space engineering research papers

In This Section

• Books/eBooks Authors
• Textbook Adoptions
• How to Order
• Journal Author
• Submission of AIAA Conference Papers to Journals
• Supplemental Materials for Journals
• Journal Scopes and Content
• Journal Prices for Members
• Meeting Paper Author
• Standards Contributors
• Basic Steps in Developing an AIAA Standard
• Standards Domains Outreach
• Publication Policies
• Ethical Standards for Publication of Aeronautics and Astronautics Research
• Copyright Clearance and Assignment
• Copyright Assignment for Works Submitted
• Rights and Permissions
• Contact Publications
• Aerospace America
• Search Publications on ARC

AIAA's journals are the best resource for students and professionals who want to stay current on the latest aerospace research and development. Reporting on the most important aerospace advances, AIAA‘s eight active technical journals feature original research papers spanning the spectrum of aerospace science and technology.

 Peer Reviewed

AIAA journals provide a panoramic journey from yesterday’s challenges through today's most important aerospace advances in research and development. Available online, AIAA’s original research papers present concepts, methods of analysis, technical knowledge, exploratory developments, and new applications. Each submission is peer-reviewed by experts in the field using a single-blind process to ensure content is accurate and of high quality.

For Reviewer consideration, please create an account in  ScholarOne Manuscripts . Where possible, please answer all questions. An Editor may contact you to review appropriate topic manuscripts based on information from your registration. Thank you.

 50+ Years of AIAA Journal Archives Online

Current and back issues of AIAA journals are available online in Aerospace Research Central (ARC).

• AIAA Journal (1963–current):  This online-only journal was launched along with AIAA in 1963, covering pioneering theoretical developments and experimental results across a far-reaching range of disciplines.
• Journal of Air Transportation   (2016–current):  AIAA’s newest online-only journal was originally published as Air Traffic Control Quarterly and is devoted to new developments in air traffic management and aviation operations of all flight vehicles, including unmanned aerial vehicles (UAVs) and space vehicles, operating in the global airspace system. The scope of the journal includes theory, applications, technologies, operations, economics, and policy. 
• Journal of Aerospace Information Systems   (2004-current):  This online-only journal (formerly known as the Journal of Aerospace Computing, Information, and Communication )describes new theoretical developments, novel applications, and case studies of advances in aerospace computing, information, and communication systems.
• Journal of Aircraft (1964–current): Focusing on major advances in aircraft technology, this journal covers major development in general aviation, military and civilian aircraft, STOL and V/STOL aircraft, subsonic, supersonic, transonic, and hypersonic aircraft as well as applications on aircraft technology to related fields.
• Journal of Energy (1977–1983):  You can now retrieve papers from this archived journal that was devoted to advancing the knowledge of terrestrial and space applications of all forms of energy, including its production, transformation, and conservation.
• Journal of Guidance, Control, and Dynamics (1978–current):  Keep pace with recent research and practical engineering applications that are guiding new generations of high-performance air and space vehicles — both manned and. This journal was originally published as the Journal of Guidance and Control.
• Journal of Hydronautics (1967–1980):  Access archived articles from this retired journal to explore theoretical and experimental knowledge of hydrodynamics, including propulsion systems and the design of underwater vehicles, highlighting the intersection between ocean and aerospace science and engineering.
• Journal of Propulsion and Power (1985–current): Discover advances in air-breathing, electric, and advanced propulsion, plus power generation and conversion, and terrestrial energy systems in the Journal of Propulsion and Power .
• Journal of Spacecraft and Rockets (1964–current): This journal features the best new work in spacecraft and missile systems (tactical and strategic), including subsystems, applications, missions, environmental interactions, and space sciences.
• Journal of Thermophysics and Heat Transfer (1987–current): Explore the latest developments in thermal energy transfer and storage — in gases, liquids, and solids.

Additional Resources

• Author Information
• AIAA’s Policies
• Numerical and Experimental Accuracy Policy
• Copyright Assignment for Works Submitted to AIAA for Publication
• ARC 'How To' Videos
• AIAA Member Journal Prices

Out of the Past: AIAA’s Predecessor Society Journal Archives

Archive copies from AIAA’s predecessor society publications also are available online in ARC .

• Bulletin of the American Interplanetary Society (1930–1932): This bimonthly newsletter of the American Interplanetary Society (later known as the American Rocket Society) shared the wonders of space travel with the fledgling society’s members.
• Astronautics (1932–1944): Published monthly by the American Interplanetary Society, this formal newsletter replaced the Bulletin of the American Interplanetary Society .
• Journal of the Aeronautical Sciences (1934–1957) and Journal of the Aerospace Sciences (1958-1962) : The monthly journal of the Institute of the Aeronautical Science (later the Institute of the Aerospace Sciences) published scientific and technical articles along with member news.
• Journal of the American Rocket Society (1945-1953) : This journal published technical articles on experiments in rocketry and scientific research and engineering development of jet propulsion devices and their application to problems of transportation and communication.
• Journal of Jet Propulsion (1954–1958): This journal published by the American Rocket Society was devoted to the advancement of the field of jet propulsion through the publication of original scientific papers and also shared society news.
• ARS Journal (1959–1962): Originally published under the title Journal of Jet Propulsion the name change of this journal reflected the ever-broadening field of interest of the American Rocket Society. Papers were chosen for publication based on their pertinence to the general field of astronautics, the future significance of the research, and importance to the members of the society and the profession at large.

Recent papers

Space agenda 2025 compendium.

Our Most Vital Asset: Space Ground Infrastructure and U.S. Foreign Relations

Strengthening a Solid Foundation: U.S. Advantages from Commercial Space

Why Transforming the Budget Structure Would Benefit Defense Space

Rational Exuberance: Understanding Value and Performance in the Space Economy

Mind the Gap: Commercial Space Stations & the ISS

Framing Space Agenda Through Strategic Foresight

Space-Enabled Capabilities for Connecting and Collaborating in the Arctic

Anticipating the New European Union Space Law

Russia’s War in Ukraine: Key Observations About Space

The Invisible Link: Key Spectrum Issues for Space

Moonstruck! International Aspirations in Cislunar Space

Strengthening the Industrial Base to Deliver the Future Defense Space Architecture

The Next Space Security Norm

Leverage and Preserve: Need for DOD to Strengthen Support for U.S. Commercial Space

Space Regulatory Reform Is a Wicked Problem Still Worth Tackling

Space Sustainability in the Context of Contested Space

Charting a Path Through the Space Arms Control Verification Challenge

The Space Development Agency and the Future of Defense Space Acquisitions

FY 2025 Defense Space Budget: Continued Emphasis on Proliferation Under a More Constrained Top-Line

Should the Space Force Have a Warfighting-Centric Culture?

High Ground or High Fantasy: Defense Utility of Cislunar Space

Lessons from the cloud: outsourcing and integrating commercial space services.

Mine Games: Securing America’s Critical Mineral Supply

(Space) Ships Passing in the Night: Translating Maritime Rules of the Road for the Space Domain

Pack It In, Pack It Out: Updating Policy and Standards for Cislunar Sustainability

Game Changer: The Great Convergence and the Future of Satellite-Enabled Direct-to-Device

South Korea Country Brief

Striking A Balance Between Safety and Scrubs

STAR: Shining Light on Space Supply Chain Risk

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Aerospace engineering articles within Nature

Article | 31 July 2024

Passive wing deployment and retraction in beetles and flapping microrobots

We find that rhinoceros beetles passively deploy and retract their hindwings without muscular activity, demonstrating this with insect-like microrobots.

• Hoang-Vu Phan
• , Hoon Cheol Park
•  &  Dario Floreano

Article | 17 July 2024

Sunlight-powered sustained flight of an ultralight micro aerial vehicle

Solar-powered, untethered, sustained flight of an ultralight micro aerial vehicle under natural sunlight conditions is achieved using an electrostatic-driven propulsion system with a high lift-to-power efficiency.

• , Jinzhe Peng
•  &  Mingjing Qi

Article 30 August 2023 | Open Access

Champion-level drone racing using deep reinforcement learning

An autonomous system is described that combines deep reinforcement learning with onboard sensors collecting data from the physical world, enabling it to fly faster than human world champion drone pilots around a race track.

• Elia Kaufmann
• , Leonard Bauersfeld
•  &  Davide Scaramuzza

Article 19 October 2022 | Open Access

Accommodating unobservability to control flight attitude with optic flow

Attitude can be extracted from optic flow when combined with a motion model that relates attitude to acceleration direction, which leads to stable flight attitude control with slight oscillations due to unobservable conditions.

• Guido C. H. E. de Croon
• , Julien J. G. Dupeyroux
•  &  Franck Ruffier

Article | 21 September 2022

Aerial additive manufacturing with multiple autonomous robots

An additive manufacturing method using a team of autonomous aerial robots allows for scalable and adaptable three-dimensional printing, and is used to deposit building materials during flight.

• Ketao Zhang
• , Pisak Chermprayong
•  &  Mirko Kovac

Article 29 June 2022 | Open Access

Optimization of avian perching manoeuvres

To perch safely, large birds minimize the distance flown after stalling when swooping up from a dive to a perch, but not the time or energy required.

• Marco KleinHeerenbrink
• , Lydia A. France
•  &  Graham K. Taylor

Article 09 March 2022 | Open Access

Birds can transition between stable and unstable states via wing morphing

Analysis of inertial characteristics across 22 bird species shows that evolution has selected for avian manoeuvrability using both stable and unstable flight dynamics.

• , V. B. Baliga
•  &  D. J. Inman

Perspective | 26 January 2022

The challenges and opportunities of battery-powered flight

The economic, technical, environmental and safety requirements of battery-powered aircraft are considered, and promising technologies and future prospects for battery innovation are discussed.

• Venkatasubramanian Viswanathan
• , Alan H. Epstein
•  &  Michael Winter

Article 17 November 2021 | Open Access

In-orbit demonstration of an iodine electric propulsion system

The successful in-orbit operation of an electric space propulsion system based on iodine, rather than the more expensive and difficult-to-store xenon, is demonstrated.

• Dmytro Rafalskyi
• , Javier Martínez Martínez
•  &  Ane Aanesland

Article | 22 September 2021

Three-dimensional electronic microfliers inspired by wind-dispersed seeds

With a design inspired by wind-dispersed seeds, a series of three-dimensional passive fliers at the macro-, meso- and microscale are realized that can bear active electronic payloads.

• Bong Hoon Kim
•  &  John A. Rogers

Article | 02 December 2020

Autonomous navigation of stratospheric balloons using reinforcement learning

Data augmentation and a self-correcting design are used to develop a reinforcement-learning algorithm for the autonomous navigation of Loon superpressure balloons in challenging stratospheric weather conditions.

• Marc G. Bellemare
• , Salvatore Candido
•  &  Ziyu Wang

Letter | 26 June 2019

Untethered flight of an insect-sized flapping-wing microscale aerial vehicle

Sustained flight of an insect-sized flapping-wing aerial vehicle weighing just 259 milligrams that does not need to fly tethered to an off-board power supply is demonstrated.

• Noah T. Jafferis
• , E. Farrell Helbling
•  &  Robert J. Wood

Letter | 21 November 2018

Flight of an aeroplane with solid-state propulsion

A solid-state propulsion system can sustain powered flight, as demonstrated by an electroaerodynamically propelled heavier-than-air aeroplane.

•  &  Steven R. H. Barrett

Letter | 19 September 2018

Glider soaring via reinforcement learning in the field

A reinforcement learning approach allows a suitably equipped glider to navigate thermal plumes autonomously in an open field.

• Gautam Reddy
• , Jerome Wong-Ng
•  &  Massimo Vergassola

Letter | 08 March 2018

Measurement of Jupiter’s asymmetric gravity field

Precise Doppler tracking of the Juno spacecraft in its polar orbit around Jupiter is used to determine the planet’s gravity harmonics, showing north–south asymmetry caused by atmospheric and interior flows.

• , W. M. Folkner
•  &  S. J. Bolton

Letter | 29 March 2017

Smart wing rotation and trailing-edge vortices enable high frequency mosquito flight

In addition to generating lift by leading-edge vortices (as used by most insects), mosquitoes also employ trailing-edge vortices and a lift mechanism from wing rotation, which enables them to stay airborne despite having a seemingly unlikely airframe.

• Richard J. Bomphrey
• , Toshiyuki Nakata
•  &  Simon M. Walker

Letter | 20 February 2017

Mechanical metamaterials at the theoretical limit of isotropic elastic stiffness

Finite-element models are used to identify a material geometry that achieves the theoretical bounds on isotropic elastic stiffness—a combination closed-cell cubic and octet foam.

• J. B. Berger
• , H. N. G. Wadley
•  &  R. M. McMeeking

News | 08 December 2010

False dawn for Japan's Venus mission

Akatsuki probe will have to survive for six more years to get a second chance of orbit.

• David Cyranoski

News | 22 October 2010

Space tourism to accelerate climate change

Scientists predict that soot from commercial space flight will change global temperatures.

News | 29 June 2010

Space capsule probed for asteroid dust

Hayabusa holds lessons for future sample-return missions.

News | 28 June 2010

Origami that folds itself

Programmable sheet puckers up spontaneously into an aeroplane or a boat.

• Philip Ball

Browse broader subjects

• Engineering

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Applications and Challenges of Artificial Intelligence in Aerospace Engineering

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Advertisement

Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review

• Review article
• Published: 16 April 2024
• Volume 31 , pages 4031–4086, ( 2024 )

Cite this article

• Kamal Hassan 1 ,
• Amit Kumar Thakur   ORCID: orcid.org/0000-0002-9929-6176 2 ,
• Gurraj Singh 3 ,
• Jaspreet Singh 4 ,
• Lovi Raj Gupta 5 &
• Rajesh Singh 6  

1351 Accesses

Explore all metrics

This research aims to comprehensively analyze the most essential uses of artificial intelligence in Aerospace Engineering. We obtained papers initially published in academic journals using a Systematic Quantitative Literature Review (SQLR) methodology. We then used bibliometric methods to examine these articles, including keyword co-occurrences and bibliographic coupling. The findings enable us to provide an up-to-date sketch of the available literature, which is then incorporated into an interpretive framework that enables AI's significant antecedents and effects to be disentangled within the context of innovation. We highlight technological, security, and economic factors as antecedents prompting companies to adopt AI to innovate. As essential outcomes of the deployment of AI, in addition to identifying the disciplinary focuses, we also identify business organizations' product innovation, process innovation, aerospace business model innovation, and national security issues. We provide research recommendations for additional examination in connection to various forms of innovation, drawing on the most critical findings from this study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

The research landscape on the artificial intelligence: a bibliometric analysis of recent 20 years

Recapitulation of Research in Artificial Intelligence: A Bibliometric Analysis

The role of artificial intelligence algorithms in information systems research: a conceptual overview and avenues for research

Data availability.

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

Insaurralde CC (2020) Artificial intelligence engineering for aerospace applications. In: AIAA/IEEE digital avionics systems conference - proceedings. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

Yao J, Tran SN, Sawyer S, Garg S (2023) Machine learning for leaf disease classification: data, techniques and applications. Springer, Dordrecht

Google Scholar  

Mariani MM, Machado I, Nambisan S (2023) Types of innovation and artificial intelligence: a systematic quantitative literature review and research agenda. J Bus Res 155:113364. https://doi.org/10.1016/j.jbusres.2022.113364

Article   Google Scholar  

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222. https://doi.org/10.1111/1467-8551.00375

Shankar A (2023) Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system. Artif Intell Rev 56:15053–15077. https://doi.org/10.1007/s10462-023-10519-y

Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK (2021) Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 54:4237–4316

De la Vega Hernández IM, Urdaneta AS, Carayannis E (2023) Global bibliometric mapping of the frontier of knowledge in the field of artificial intelligence for the period 1990–2019. Springer, Dordrecht

Book   Google Scholar  

Peng X, Dai J (2020) A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017. Artif Intell Rev 53:199–255. https://doi.org/10.1007/s10462-018-9652-0

Zhang G, Hsu L-T (2018) Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system. Aerosp Sci Technol 80:368–380. https://doi.org/10.1016/j.ast.2018.07.026

Baldoni R, Montanari L, Rizzuto M (2015) On-line failure prediction in safety-critical systems. Future Gen Comput Syst 45:123–132. https://doi.org/10.1016/j.future.2014.11.015

Gertsvolf D, Berardi U, Horvat M (2021) Aerial infrared thermography and artificial intelligence-based fault detection and diagnosis methods for building energy systems: a review of the state-of-the-art. In: K.A. TA, M.T. S, D.E. A (eds) ZEMCH international conference. ZEMCH Network, Building Science Graduate Program, Department of Architectural Science, Ryerson University, Canada, pp 530–540

Ahmed HO (2023) Coarse grained FLS-based processor with prognostic malfunction feature for UAM drones using FPGA. In: Integrated communications, navigation and surveillance conference, ICNS. Institute of Electrical and Electronics Engineers Inc.

Nahar P, Wu KH, Mei S, Ghoghari H, Srinivasan P, Lee Y L, Guan X (2017, August) Autonomous UAV forced graffiti detection and removal system based on machine learning. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp 1–8. IEEE.

Yang Z, Lee WC, Chan HN, Ge M (2022) A real-time tunnel surface inspection system using edge-AI on drone. In: 2022 IEEE International conference on Mechatronics and Automation, ICMA 2022. Institute of Electrical and Electronics Engineers Inc., Hong Kong Productivity Council, Robotics and Artificial Intelligence Division, 78 Tak Chee Ave, Kowloon Tong, Hong Kong, pp 749–754

Chen Y-W, Chen K-Y, Fang Y-L (2021) Aiming point guidance algorithm based on proportional navigation guidance scheme. Control Eng Appl Informatics 23:72–81

Candon M, Esposito M, Fayek H, Levinski O, Koschel S, Joseph N, Marzocca P (2022) Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning. Mech Syst Signal Process 171:108809. https://doi.org/10.1016/j.ymssp.2022.108809

Sassu A, Motta J, Deidda A, Ghiani L, Carlevaro A, Garibotto G, Gambella F (2023) Artichoke deep learning detection network for site-specific agrochemicals UAS spraying. Comput Electron Agric 213:108185. https://doi.org/10.1016/j.compag.2023.108185

Matuszewski J, Pietrow D (2019) Recognition of electromagnetic sources with the use of deep neural networks. In: P. K (ed) Proceedings of SPIE - The International Society for Optical Engineering. SPIE

Maleki KN, Ashenayi K, Hook LR, Fuller JG, Hutchins N (2016, September) A reliable system design for nondeterministic adaptive controllers in small UAV autopilots. In 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp 1–5. IEEE

Reddy A, Indragandhi V, Ravi L, Subramaniyaswamy V (2019) Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2019.07.051

Munawar HS, Ullah F, Heravi A, Thaheem MJ, Maqsoom A (2021) Inspecting buildings using drones and computer vision: a machine learning approach to detect cracks and damages. Drones 6(1):5. https://doi.org/10.3390/drones6010005

Lee H, Puranik TG, Mavris DN (2021) Deep spatio-temporal neural networks for risk prediction and decision support in aviation operations. J Comput Inf Sci Eng. DOI 10(1115/1):4049992

Eltoukhy AE, Chan FT, Chung SH, Niu B, Wang XP (2017) Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations. Ind Manag Data Syst 117(10):2142–2170. https://doi.org/10.1108/IMDS-11-2016-0475

Cruciol LLBV, Weigang L, De Barros AG, Koendjbiharie MW (2015) Air holding problem solving with reinforcement learning to reduce airspace congestion. J Adv Transp 49:616–633. https://doi.org/10.1002/atr.1293

Jenab K, Pineau J (2018) Automation of air traffic management using fuzzy logic algorithm to integrate unmanned aerial systems into the national airspace. Int J Electr Comput Eng 8:3169–3178. https://doi.org/10.11591/IJECE.V8I5.PP3169-3178

Richards RA (2002) Application of multiple artificial intelligence techniques for an aircraft carrier landing decision support tool. IEEE Int Conf Fuzzy Syst 1:7–11. https://doi.org/10.1109/fuzz.2002.1004950

Shahpar S (2020) Building digital twins to simulate manufacturing variation. In: Proceedings of the ASME Turbo Expo

Benzon HH, Chen X, Belcher L, Castro O, Branner K, Smit J (2022) An operational image-Based digital twin for large-scale structures. Appl Sci 12(7):3216. https://doi.org/10.3390/app12073216

Apostolidis A, Stamoulis KP (2021) An AI-based digital twin case study in the MRO sector. In: Transportation research procedia. pp 55–62

Apostolidis A, Bouriquet N, Stamoulis KP (2022) AI-based exhaust gas temperature prediction for trustworthy safety-critical applications. Aerospace. https://doi.org/10.3390/aerospace9110722

Ruotsalainen M, Jylha J, Visa A (2016) Minimizing fatigue damage in aircraft structures. IEEE Intell Syst 31:22–29. https://doi.org/10.1109/MIS.2016.23

Tseranidis S, Brown NC, Mueller CT (2016) Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures

Tong C, Yin X, Wang S, Zheng Z (2018) A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data. Future Gen Comput Syst 88:552–558. https://doi.org/10.1016/j.future.2018.06.023

Martinsen M, Zhou Y, Dahlquist E, Yan J, Kyprianidis K (2023) Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future. Appl Energy 339:120988. https://doi.org/10.1016/j.apenergy.2023.120988

Gao J, Guo J, Dai A, Situ G (2023) Optical system design: from iterative optimization to artificial intelligence. Zhongguo Jiguang. https://doi.org/10.3788/CJL230497

Waisberg E, Ong J, Kamran SA, Paladugu P, Zaman N, Lee AG, Tavakkoli A (2023) Transfer learning as an AI-based solution to address limited datasets in space medicine. Life Sci Space Res 36:36–38. https://doi.org/10.1016/j.lssr.2022.12.002

Panda B, Leite M, Biswal BB, Niu X, Garg A (2018) Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures. Measurement 116:495–506. https://doi.org/10.1016/j.measurement.2017.11.037

Huang H, Hu Z, Lu Z, Wen X (2023) Network-scale traffic signal control via multiagent reinforcement learning with deep spatiotemporal attentive network

Buster G, Siratovich P, Taverna N, Rossol M, Weers J, Blair A, Akerley J (2021) A new modeling framework for geothermal operational optimization with machine learning (Gooml). Energies 14(20):6852

Siyaev A, Valiev D, Jo G-S (2023) Interaction with industrial digital twin using neuro-symbolic reasoning. Sensors. https://doi.org/10.3390/s23031729

Solomon A, Crawford Z (2021) Transitioning from legacy air traffic management to airspace management through secure, cloud-native automation solutions. In: AIAA/IEEE digital avionics systems conference - proceedings. Institute of Electrical and Electronics Engineers Inc.

Roscoe S, Cousins PD, Handfield R (2023) Transitioning additive manufacturing from rapid prototyping to high-volume production: a case study of complex final products. J Prod Innov Manag 40:554–576. https://doi.org/10.1111/jpim.12673

Natali A, Padalkar MG, Messina V, Salvatore W, Morerio P, Del Bue A, Beltrán-González C (2023) Artificial Intelligence tools to predict the level of defectiveness of existing bridges. Proc Struct Integr 44:2020–2027

Roy S, Maji A (2022) Sampling-based modified ant colony optimization method for high-speed rail alignment development. Comput Aid Civ Infrastruct Eng 37(11):1417–1433

Li W, Zhang X, Huang B, Chen Y, Zhang R, BalaMurugan S (2022) Research on the control method of unmanned helicopter under the background of artificial intelligence. J Interconnect Netw 22(Supp02):2143019. https://doi.org/10.1142/S0219265921430192

Ye L, Lu Y, Su Z, Meng G (2005) Functionalized composite structures for new generation airframes: a review. Compos Sci Technol 65:1436–1446. https://doi.org/10.1016/j.compscitech.2004.12.015

Jeppu Y, Raman R (2022) A framework for teaching safety critical artificially intelligent control systems to undergrads. In: SAE technical papers. Honeywell Technology Solutions Lab., India

Reitmann S, Schultz M (2022) An adaptive framework for optimization and prediction of air traffic management (sub-)systems with machine learning. Aerospace. https://doi.org/10.3390/aerospace9020077

Liu Y (2021) Development of hyperspectral imaging remote sensing technology. Natl Remote Sens Bull 25:439–459. https://doi.org/10.11834/jrs.20210283

Li K, Zhang R, Wang H, Yu F (2021) Multi-intelligent connected vehicle longitudinal collision avoidance control and exhaust emission evaluation based on parallel theory. Process Saf Environ Prot 150:259–268. https://doi.org/10.1016/j.psep.2021.04.001

Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069

Carou D (2021) Aerospace Transformation through Industry 4.0 Technologies. In: SpringerBriefs in Applied Sciences and Technology. Escola de Enxeñaría Aeronáutica e do Espazo, Universidade de Vigo, Ourense, Spain, pp 17–46

Hu M, Cao E, Huang H, Zhang M, Chen X, Chen M (2023) AIoTML: a unified modeling language for aiot-based cyber-physical systems. In: IEEE transactions on computer-aided design of integrated circuits and systems

Bilen T, Canberk B, Duong TQ (2023) Digital twin evolution for hard-to-follow aeronautical ad-hoc networks in beyond 5G. IEEE Commun Stand Mag 7:4–12. https://doi.org/10.1109/MCOMSTD.0001.2200040

Indragandhi V, Ashok Kumar L (2018) Artificial intelligence based speed control of SRM for hybrid electric vehicles. In: 2018 8th International conference on Power and Energy Systems, ICPES 2018. Electrical Engineering, Vellore Institute of Technology, Vellore, India, pp 65–68

Mochalov V, Grigorieva O, Zhukov D, Markov A, Saidov A (2020) Remote sensing image processing based on modified fuzzy algorithm. In: Artificial intelligence and bioinspired computational methods: proceedings of the 9th computer science on-line conference 2020, Vol 2–9, pp 563–572. Springer

Sutthithatip S, Perinpanayagam S, Aslam S (2022) (Explainable) Artificial intelligence in aerospace safety-critical systems. In: IEEE aerospace conference proceedings. Cranfield University, Integrated Vehicle Health Management (IVHM) Centre, United Kingdom

Murray M (2003) Future directions in control in an information-rich world. IEEE Control Syst 23:20–33. https://doi.org/10.1109/MCS.2003.1188769

Ma Z, Kang S, Ma J, Shao L, Wei A, Liang C, Ji Z (2019) High-performance and rapid-response electrical heaters based on ultraflexible, heat-resistant, and mechanically strong aramid nanofiber/Ag nanowire nanocomposite papers. ACS Nano 13(7):7578–7590. https://doi.org/10.1021/acsnano.9b00434

Wilson GF, Russell CA (2003) Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. In: Human factors. U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, USA, pp 635–643

Cao MS, Wang XX, Zhang M, Cao WQ, Fang XY, Yuan J (2020) Variable-temperature electron transport and dipole polarization turning flexible multifunctional microsensor beyond electrical and optical energy. Adv Mater 32(10):1907156. https://doi.org/10.1002/adma.201907156

Reigber A, Scheiber R, Jager M, Prats-Iraola P, Hajnsek I, Jagdhuber T, Moreira A (2012) Very-high-resolution airborne synthetic aperture radar imaging: Signal processing and applications. Proc IEEE 101(3):759–783. https://doi.org/10.1109/JPROC.2012.2220511

Tracey B, Duraisamy K, Alonso JJ (2015) A machine learning strategy to assist turbulence model development. In: 53rd AIAA aerospace sciences meeting. Stanford University, USA

Curran R, Raghunathan S, Price M (2004) Review of aerospace engineering cost modelling: the genetic causal approach. Prog Aerosp Sci 40:487–534. https://doi.org/10.1016/j.paerosci.2004.10.001

Izzo D, Märtens M, Pan B (2019) A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodynamics 3:287–299. https://doi.org/10.1007/s42064-018-0053-6

Duraisamy K, Zhang ZJ, Singh AP (2015) New approaches in turbulence and transition modeling using data-driven techniques. In: 53rd AIAA aerospace sciences meeting. Department of Aerospace Engineering, University of Michigan, Ann Arbor, 48109, MI, USA

Woodward D, Hobbs M, Gilbertson JA, Cohen N (2021) Uncertainty quantification for trusted machine learning in space system cyber security. Proceedings - 8th IEEE international conference on Space Mission Challenges for Information Technology, SMC-IT 2021. Institute of Electrical and Electronics Engineers Inc., The Aerospace Corporation, Data Science and Artificial Intelligence Department, El Segundo, CA, USA, pp 38–43

Pelton JN, Dahlstrom E (2020) Small satellites and governmental role in development of new technology, services, and markets. Springer, Berlin

Han B, Wang L, Deng Z, Shi Y, Yu J (2022) Source emission and attribution of a large airport in Central China. Sci Total Environ 829:154519. https://doi.org/10.1016/j.scitotenv.2022.154519

Erickson JD, Goode R, Grimm KA, Hess CW, Norsworthy RS, Anderson GD, Phinney DE (1992, March) Technology test results from an intelligent, free-flying robot for crew and equipment retrieval in space. In Cooperative Intelligent Robotics in Space II, Vol 1612, pp 402–413. SPIE

Virágh C, Vásárhelyi G, Tarcai N, Szörényi T, Somorjai G, Nepusz T, Vicsek T (2014) Flocking algorithm for autonomous flying robots. Bioinspir Biomim 9(2):025012. https://doi.org/10.1088/1748-3182/9/2/025012

Clachar SA (2015) Identifying and analyzing atypical flights by using supervised and unsupervised approaches. Transp Res Rec 2471:10–18. https://doi.org/10.3141/2471-02

Abdulrahman Y, Parezanovic V, Svetinovic D (2022) AI-blockchain systems in aerospace engineering and management: review and challenges. In: 2022 30th Telecommunications Forum, TELFOR 2022 - proceedings. Khalifa University, Aerospace Engineering, Abu Dhabi, United Arab Emirates

da Silva LM, Menezes HBDB, Luccas MDS, Mailer C, Pinto ASR, Boava A, Branco KRLJC (2022) Development of an efficiency platform based on MQTT for UAV controlling and DoS attack detection. Sensors 22(17):6567. https://doi.org/10.3390/s22176567

Maksymov VO, Yurchenko OI (2018) Forecast of demand for aviation maintenance and air navigation specialists for the next 20 years. In: 2018 IEEE 5th international conference on Methods and Systems of Navigation and Motion Control, MSNMC 2018 - proceedings. Institute of Electrical and Electronics Engineers Inc., pp 110–113

Deng T, Li Y, Chen J, Liu X, Wang L (2021) Informed machine learning-based machining parameter planning for aircraft structural parts. Int J Adv Manuf Technol 117(11):3563–3575. https://doi.org/10.1007/s00170-021-07861-2

Tappe M. Dose D, Oelsch M, Karimi M, Hösch L, Heller L, Bachmeir C (2022, April) UAS-based autonomous visual inspection of airplane surface defects. In: NDE 4.0, predictive maintenance, and communication and energy systems in a globally networked world, Vol 12049, pp 8–21. SPIE

He F, Yuan L, Mu H, Ros M, Ding D, Pan Z, Li H (2023) Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review. Robot Comput Integr Manuf 82:102525. https://doi.org/10.1016/j.rcim.2023.102525

Toby T, Gopalakrishnan U, Rao SN (2022) A deeper CNN approach for detection of collapsed buildings in drone images. In: Proceedings - 2022 5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022. pp 404–410

Aust J, Shankland S, Pons D, Mukundan R, Mitrovic A (2021) Automated defect detection and decision-support in gas turbine blade inspection. Aerospace 8(2):30. https://doi.org/10.3390/aerospace8020030

Breda P, Markova R, Abdin AF, Mantı NP, Carlo A, Jha D (2023) An extended review on cyber vulnerabilities of AI technologies in space applications: technological challenges and international governance of AI. J Space Saf Eng. https://doi.org/10.1016/j.jsse.2023.08.003

Liu D, Liao T, Sun H, Ren F (2021) Research progress and development direction of Chinese remote sensing software: taking PIE as an example. J Image Graph 26:1169–1178. https://doi.org/10.11834/jig.200125

Hamilton D, Watkins L, Zanlongo S, Leeper C, Sleight R, Silbermann J, Kornegay K (2021, August) Assuring autonomous UAS traffic management systems using explainable, fuzzy logic, black box monitoring. In: 2021 10th International conference on Information and Automation for Sustainability (ICIAfS), pp 470–476. IEEE

McCall T, Seyed Alavi K, Rana L, Chudoba B (2018) Artificial intelligent research assistant for aerospace design synthesis—solution logic. In: 22nd AIAA International Space Planes and Hypersonics Systems and Technologies Conference. AVD Laboratory, UT Arlington, Dept. of Mechanical and Aerospace Engineering, Arlington, 76019, TX, USA

Yang D, Du P, Zhong M, Mao W (2020) A real-time fusion method of external trajectory measurement data based on variable difference method. China Satellite Maritime Tracking, Control Department, Jiangyin, China, pp 574–577

Bavle H, Sanchez-Lopez JL, Cimarelli C, Tourani A, Voos H (2023) From slam to situational awareness: challenges and survey. Sensors 23(10):4849. https://doi.org/10.3390/s23104849

Ma W, Lu J (2023) Research progress and challenges of electromagnetic launch technology. Diangong Jishu Xuebao 38:3941–3959. https://doi.org/10.19595/j.cnki.1000-6753.tces.230470

Rababaah H, Shirkhodaie A (2008) Human-posture classification for intelligent visual surveillance systems. In: Proceedings of SPIE - The International Society for Optical Engineering. Center of Excellence for Battlefield Sensor Fusion, Dept. of Mechanical and Manufacturing Engineering, Tennessee State University, Nashville, USA

Alhafnawi M, Salameh HB, Masadeh AE, Al-Obiedollah H, Ayyash M, El-Khazali R, Elgala H (2023) A survey of indoor and outdoor uav-based target tracking systems: current status, challenges, technologies, and future directions. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3292302

Oruc R, Sahin O, Baklacioglu T (2022) Fuel flow rate modeling for descent using cuckoo search algorithm: a case study for point merge system procedure at Istanbul airport. Aircr Eng Aerosp Technol 94:824–833. https://doi.org/10.1108/AEAT-08-2021-0246

Fang S, Ru Y, Liu Y, Hu C, Chen X, Liu B (2021) Route planning of helicopters spraying operations in multiple forest areas. Forests 12(12):1658. https://doi.org/10.3390/f12121658

Kalaichelvi P, Akila V, Rani TP (2021) Big data in multi-decision making system of the aerospace industry. In: a Closer Look at Big Data Analytics. Nova Science Publishers, Inc., Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu, India, pp 69–109

Bijjahalli S, Sabatini R, Gardi A (2020) Advances in intelligent and autonomous navigation systems for small UAS. Prog Aerosp Sci. https://doi.org/10.1016/j.paerosci.2020.100617

Sha W, Li Y, Tang S, Tian J, Zhao Y, Guo Y, Cheng S (2021) Machine learning in polymer informatics. InfoMat 3(4):353–361. https://doi.org/10.1002/inf2.12167

Cheung HC, De Louche C, Komorowski M (2023) Artificial intelligence applications in space medicine. Aerosp Med Hum Perform 94:610–622. https://doi.org/10.3357/AMHP.6178.2023

Fysikopoulos A, Alexopoulos T, Pastras G, Stavropoulos P, Chryssolouris G (2015, November) On the design of a sustainable production line: the MetaCAM tool. In: ASME international mechanical engineering congress and exposition, Vol 57588, p V015T19A015. American Society of Mechanical Engineers

Bowman M, Kesawan S, Sivapalan S, Sivaprakasam T (2021) Barriers in implementing the convolutional neural network damage detector. In: C.M. W, S. K, V. D (eds) Lecture Notes in Civil Engineering. Springer Science and Business Media Deutschland GmbH, School of Civil Engineering and Built Environment, Queensland University of Technology, QLD, Australia, pp 1333–1340

Anahara D, Ohmori S, Yoshimoto K (2019) Research on flight scheduling considering flight time arrangements. J Japan Ind Manag Assoc 70:147–156. https://doi.org/10.11221/jima.70.147

Liu J, Hu C, Zhou J, Ding W (2022) Object detection algorithm based on lightweight YOLOv4 for UAV. In: 2022 7th international conference on Intelligent Computing and Signal Processing, ICSP 2022. Institute of Electrical and Electronics Engineers Inc., Air Force Aviation University, Flight Research Department, Jilin, Changchun, China, pp 425–429

Le AV, Parween R, Elara Mohan R, Nhan NHK, Enjikalayil Abdulkader R (2020) Optimization complete area coverage by reconfigurable hTrihex tiling robot. Sensors 20(11):3170

Ranasinghe K, Sabatini R, Gardi A, Bijjahalli S, Kapoor R, Fahey T, Thangavel K (2022) Advances in Integrated System Health Management for mission-essential and safety-critical aerospace applications. Prog Aerosp Sci 128:100758. https://doi.org/10.1016/j.paerosci.2021.100758

Cecen RK, Cetek C, De Armas J (2019) A two-step approach for airborne delay minimization using pretactical conflict resolution in free-route airspace. J Adv Transp. https://doi.org/10.1155/2019/4805613

Tang YM, Ip AWH, Li W (2022) Artificial intelligence approach for aerospace defect detection using single-shot multibox detector network in phased array ultrasonic. In: IoT and Spacecraft Informatics. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China, pp 1–27

Fan B, Zhang R (2017) Unmanned aircraft system and artificial intelligence. 武汉大学学报 (信息科学版) 42:1523–1529

Tikhonov AI, Sazonov AA, Kuzmina-Merlino I (2022) Digital production and artificial intelligence in the aircraft industry. Russ Eng Res 42:412–415. https://doi.org/10.3103/S1068798X22040293

Brandoli B, de Geus AR, Souza JR, Spadon G, Soares A, Rodrigues JF Jr, Matwin S (2021) Aircraft fuselage corrosion detection using artificial intelligence. Sensors 21(12):4026. https://doi.org/10.3390/s21124026

Schirmer S, Torens C, Nikodem F, Dauer J (2018) Considerations of artificial intelligence safety engineering for unmanned aircraft. Lecture Notes in Computer Science (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 11094 LNCS:465–472. https://doi.org/10.1007/978-3-319-99229-7_40

Tikhonov A, Sazonov A (2021) Digitalization and application of artificial intelligence in aircraft. In: AIP conference proceedings. AIP

Garcia AB, Babiceanu RF, Seker R (2021) Artificial intelligence and machine learning approaches for aviation cybersecurity: an overview. In: 2021 Integrated Communications Navigation and Surveillance Conference (ICNS). IEEE, pp 1–8

Ukwandu E, Ben-Farah MA, Hindy H, Bures M, Atkinson R, Tachtatzis C, Bellekens X (2022) Cyber-security challenges in aviation industry: a review of current and future trends. Information 13(3):146. https://doi.org/10.3390/info13030146

Gura D, Khoroshko A, Sakulyeva T, Krivolapov S (2020) Intelligent data processing for navigating drones. J Adv Res Dyn Control Syst 12:396–401. https://doi.org/10.5373/JARDCS/V12SP2/SP20201086

Ju S-W (2022) A study on the influence of big data-based quality on satisfaction and repurchase intention. J Syst Manag Sci 12:286–317. https://doi.org/10.33168/JSMS.2022.0315

Ahmed HO (2023) Fault tolerant processing unit using gamma distribution sliding window for autonomous landing guidance system. In: 21st IEEE interregional NEWCAS conference, NEWCAS 2023 - proceedings. Institute of Electrical and Electronics Engineers Inc.

Zijian HU, Xiaoguang GAO, Kaifang WAN, Evgeny N, Jinliang LI (2023) Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments. Chin J Aeronaut 36(5):377–391. https://doi.org/10.1016/j.cja.2022.09.008

Raja G, Essaky S, Ganapathisubramaniyan A, Baskar Y (2023) Nexus of deep reinforcement learning and leader-follower approach for AIoT enabled aerial networks. IEEE Trans Ind Informatics 19:9165–9172. https://doi.org/10.1109/TII.2022.3226529

Kong W-R, Zhou D-Y, Zhou Y, Zhao Y-Y (2023) Hierarchical reinforcement learning from competitive self-play for dual-aircraft formation air combat. J Comput Des Eng 10:830–859. https://doi.org/10.1093/jcde/qwad020

Kökhan A, Kökhan S, Gökdalay M (2023) An algorithmic application with flexible airspace approach. Aircr Eng Aerosp Technol 95:1045–1053. https://doi.org/10.1108/AEAT-06-2022-0147

Basmaji T, Yaghi M, Alhalabi M, Rashed A, Zia H, Mahmoud M, Ghazal M (2023) AI-powered health monitoring of anode baking furnace pits in aluminum production using autonomous drones. Eng Appl Artif Intell 122:106143. https://doi.org/10.1016/j.engappai.2023.106143

Liu Z, Xu Q, Shi Y, Xu K, Tan Q (2022, March) Generation method of control strategy for aircrafts based on hierarchical reinforcement learning. In: Artificial intelligence in China: proceedings of the 3rd international conference on artificial intelligence in China, pp 109–116. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_14

Wilson AN, Kumar A, Jha A, Cenkeramaddi LR (2022) Embedded sensors, communication technologies, computing platforms and machine learning for UAVs: a review. IEEE Sens J 22:1807–1826. https://doi.org/10.1109/JSEN.2021.3139124

Cetin E, Barrado C, Pastor E (2021) Counter a drone and the performance analysis of deep reinforcement learning method and human pilot. In: AIAA/IEEE digital avionics systems conference - proceedings. Institute of Electrical and Electronics Engineers Inc.

Asadi E, Chen W, Fricke H (2021) Estimating stochastic air transport process times using the Fuzzy Critical Path Method: Determination of the Estimated aircraft Total Turnaround Time (ETTT). In: 14th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2021. EUROCONTROL

Zhang Y, Tian H, Huang X, Ma C, Wang L, Liu H, Lan Y (2021) Research progress and prospects of agricultural aero-bionic technology in China. Appl Sci 11(21):10435. https://doi.org/10.3390/app112110435

Dalmau R, Allard E (2020) Air traffic control using message passing neural networks and multi-agent reinforcement learning. In: SESAR Innovation Days. SESAR Joint Undertaking

Li Y, Feng B, Zhang Y (2020) Research on yaw crossing point optimization based on genetic algorithm. In: BX, KM (eds) Proceedings of 2020 IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020. Institute of Electrical and Electronics Engineers Inc., pp 1173–1178

Wang R, Zou D, Pei L, Liu P, Xu C (2016) Velocity prediction for multi-rotor UAVs based on machine learning. In: China Satellite Navigation Conference (CSNC) 2016 proceedings: Volume II, pp 487–500. Springer, Singapore. https://doi.org/10.1007/978-981-10-0937-2_41

Widrow B, Aragon JC (2013) Cognitive memory. Neural Netw 41:3–14. https://doi.org/10.1016/j.neunet.2013.01.016

Bautista-Hernández J, Martín-Prats MÁ (2023) Monte Carlo simulation applicable for predictive algorithm analysis in aerospace. IFIP Adv Inf Commun Technol 678:243–256. https://doi.org/10.1007/978-3-031-36007-7_18

Chen D, Yuan L, Zhang Y, Yan J, Kahaner D (2020) HPC software capability landscape in China. Int J High Perform Comput Appl 34(1):115–153. https://doi.org/10.1177/1094342018760614

Luo X, Wan H, Wu C, Zheng Y, Zhou F (2020, October) A comprehensive experiment to enhance multidisciplinary engineering ability via UAVs visual navigation. In: 2020 IEEE Frontiers in Education Conference (FIE), pp 1–5. IEEE.

Yetkin S, Abuhanieh S, Yigit S (2024) Investigation on the abilities of different artificial intelligence methods to predict the aerodynamic coefficients. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2023.121324

Jwo JS, Hsieh HY, Lee CH, Lin CS, Wang PW, Hong CY, Hsu HC (2022) Simulation and modeling of a data twin service for the autoclave curing process. IEEE Access 10:111879–111887. https://doi.org/10.1109/ACCESS.2022.3216062

Shafi A, Latham I (2021) Digitalization challenges in composites. Compos Adv Mater Expo 2021:1207–1220

Zhu Z, Lu H, Zhao W, Chang X (2023) Materials, performances and applications of electric heating films. Renew Sustain Energy Rev 184:113540. https://doi.org/10.1016/j.rser.2023.113540

Wang Y-J, Ma Z, Tang X-H, Wang Z-P (2019) Autonomous obstacle avoidance algorithm of UAVs for automatic terrain following application. In: 2019 IEEE International conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019. pp 309 – 314

Castro DG, Garcia EV (2021) Safety challenges for integrating U-space in urban environments. In: 2021 International conference on Unmanned Aircraft Systems, ICUAS 2021. pp 1258–1267

Li S, van der Horst E, Duernay P, De Wagter C, de Croon GC (2020) Visual model-predictive localization for computationally efficient autonomous racing of a 72-g drone. J Field Robot 37(4):667–692. https://doi.org/10.1002/rob.21956

Kersandt K, Muñoz G, Barrado C (2018) Self-training by reinforcement learning for full-autonomous drones of the future. In: AIAA/IEEE Digital Avionics Systems Conference - proceedings

Peterson M, Du M, Springle B, Black J (2022) SpaceDrones 2.0—hardware-in-the-loop simulation and validation for orbital and deep space computer vision and machine learning tasking using free-flying drone platforms. Aerospace. https://doi.org/10.3390/aerospace9050254

Haley J, Tucker J, Nesper J, Daniel B, Fish T (2023, June) Multi-agent collaboration environment simulation. In: Synthetic data for artificial intelligence and machine learning: tools, techniques, and applications, Vol 12529, pp 197–202. SPIE

Chen Y, Zhang J, Yang Q, Zhou Y, Shi G, Wu Y (2020, December) Design and verification of UAV maneuver decision simulation system based on deep q-learning network. In: 2020 16th International conference on Control, Automation, Robotics and Vision (ICARCV), pp 817–823. IEEE

Wang J, Ding G, Wang H (2018) HF communications: past, present, and future. China Commun 15:1–9. https://doi.org/10.1109/CC.2018.8456447

Ivancevic V, Yue Y (2016) Hamiltonian dynamics and control of a joint autonomous land–air operation. Nonlinear Dyn 84:1853–1865. https://doi.org/10.1007/s11071-016-2610-y

Article   MathSciNet   Google Scholar  

Liang Z, Li Q, Fu G (2023) Multi-UAV collaborative search and attack mission decision-making in unknown environments. Sensors. https://doi.org/10.3390/s23177398

Bany Salameh H, Alhafnawi M, Masadeh A, Jararweh Y (2023) Federated reinforcement learning approach for detecting uncertain deceptive target using autonomous dual UAV system. Inf Process Manag. https://doi.org/10.1016/j.ipm.2022.103149

Hussaini H, Yang T, Bai G, Urrutia-Ortiz M, Bozhko S (2023) Artificial intelligence-based hierarchical control design for current sharing and voltage restoration in dc microgrid of the more electric aircraft. IEEE Trans Transp Electrific. https://doi.org/10.1109/TTE.2023.3289773

Knoblock EJ, Apaza RD, Gasper MR, Li H, Han R, Wang Z, Adams N (2023) Intelligent spectrum management for future aeronautical communications. IEEE Aerosp Electron Syst Mag. https://doi.org/10.1109/MAES.2022.3233817

Stamate M-A, Pupăză C, Nicolescu F-A, Moldoveanu C-E (2023) Improvement of hexacopter UAVs attitude parameters employing control and decision support systems. Sensors. https://doi.org/10.3390/s23031446

Wang G, Zou C, Zhang C, Pan C, Song J, Yang F (2022) Aircarft signal feature extraction and recognition based on deep learning. IEEE Trans Veh Technol 71(9):9625–9634. https://doi.org/10.1109/TVT.2022.3180483

Priyanka EB, Thangavel S, Sagayam KM, Elngar AA (2022) Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications. Int J Syst Assur Eng Manag 13:1254–1267. https://doi.org/10.1007/s13198-021-01425-z

Guan X, Lou S, Li H, Tang T (2021) Intelligent control of quad-rotor aircrafts with a STM32 microcontroller using deep neural networks. Ind Rob 48:700–709. https://doi.org/10.1108/IR-10-2020-0239

Li L, Zhang Z, Xue K, Wang M, Pan M, Han Z (2020) AI-aided downlink interference control in dense interference-aware drone small cells networks. IEEE Access 8:15110–15122. https://doi.org/10.1109/aCCESS.2020.2966740

Lin Y, Zhang J-W, Liu H (2018) An algorithm for trajectory prediction of flight plan based on relative motion between positions. Front Inf Technol Electron Eng 19:905–916. https://doi.org/10.1631/FITEE.1700224

Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R (2018) Damage identification in aircraft structures with self-powered sensing technology: a machine learning approach. Struct Control Health Monit 25(12):e2262. https://doi.org/10.1002/stc.2262

Loukas G, Vuong T, Heartfield R, Sakellari G, Yoon Y, Gan D (2017) Cloud-based cyber-physical intrusion detection for vehicles using deep learning. IEEE Access 6:3491–3508. https://doi.org/10.1109/ACCESS.2017.2782159

Kurmis M, Andziulis A, Dzemydiene D, Jakovlev S, Voznak M, Gricius G (2015) Cooperative context data acquisition and dissemination for situation identification in vehicular communication networks. Wireless Pers Commun 85:49–62

Richards DC, Salmon JL, Dickerson TJ, Mattson CA, Neff WJ (2023) A decision support system for multi-stakeholder exploration of the airship design space. J Defense Model Simul 1:15485129231164416

Eroglu O, Yilmaz G (2013) A novel fast and accurate algorithm for Terrain Referenced UAV localization. In: 2013 International conference on Unmanned Aircraft Systems, ICUAS 2013 - conference proceedings. pp 660–667

Hassanien AE, Darwish A, Abdelghafar S (2020) Machine learning in telemetry data mining of space mission: basics, challenging and future directions. Artif Intell Rev 53:3201–3230. https://doi.org/10.1007/s10462-019-09760-1

Arzo ST, Sikeridis D, Devetsikiotis M, Granelli F, Fierro R, Esmaeili M, Akhavan Z (2022) Essential technologies and concepts for massive space exploration: challenges and opportunities. IEEE Trans Aerosp Electron Syst 59(1):3–29

Purcell TAR, Scheffler M, Ghiringhelli LM, Carbogno C (2023) Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence

Rajput MA, Alyami S, Ahmed QA, Alshahrani H, Asiri Y, Shaikh A (2023) Improved learning-based design space exploration for approximate instance generation. IEEE Access 11:18291–18299

Mirra G, Pugnale A (2022) Exploring a design space of shell and tensile structures generated by AI from historical precedents. J Int Assoc Shell Spatial Struct 63(3):172–188

Kim DW (2022) Mars space exploration and astronautical religion in human research history: psychological countermeasures of long-term astronauts

Tragoudaras A, Stoikos P, Fanaras K, Tziouvaras A, Floros G, Dimitriou G, Stamoulis G (2022) Design space exploration of a sparse mobilenetv2 using high-level synthesis and sparse matrix techniques on FPGAs. Sensors 22(12):4318

Das S, Doppa JR, Pande PP, Chakrabarty K (2017) Design-space exploration and optimization of an energy-efficient and reliable 3-D small-world network-on-chip

Yao E, Basu A (2017) VLSI extreme learning machine: a design space exploration

Colby M, Yliniemi L, Tumer K (2016) Autonomous multiagent space exploration with high-level human feedback

Lei D, Zhong S-S (2013) MRO oriented civil aircraft engine removal date prediction system. Jisuanji Jicheng Zhizao Xitong 19:1715–1720

Liu Z, Mrad N (2014) Data fusion for the diagnostics, prognostics, and health management of aircraft systems. Adv Intell Syst Comput 215:389–399. https://doi.org/10.1007/978-3-642-37835-5_34

Kong C (2014) Review on advanced health monitoring methods for aero gas turbines using model based methods and artificial intelligent methods. Int J Aeronaut Sp Sci 15:123–137. https://doi.org/10.5139/IJASS.2014.15.2.123

Chen X, Ren H, Liu J (2013) Intelligent structural rating system based on backpropagation network. J Aircr 50:947–951. https://doi.org/10.2514/1.C032085

Dangut MD, Skaf Z, Jennions IK (2021) An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA Trans 113:127–139. https://doi.org/10.1016/j.isatra.2020.05.001

Siraskar R, Kumar S, Patil S, Bongale A, Kotecha K (2023) Reinforcement learning for predictive maintenance: a systematic technical review. Artif Intell Rev 56(11):12885–12947

Tamilselvan P, Wang P (2013) Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf 115:124–135. https://doi.org/10.1016/j.ress.2013.02.022

Zhang Q, Chan FTS, Fu X (2023) Improved ant colony optimization for the operational aircraft maintenance routing problem with cruise speed control. J Adv Transp. https://doi.org/10.1155/2023/8390619

Abidi MH, Mohammed MK, Alkhalefah H (2022) Predictive Maintenance Planning for Industry 4.0 using machine learning for sustainable manufacturing. Sustain. https://doi.org/10.3390/su14063387

Olsson E, Candell O, Funk P, Sohlberg R (2022) Assessment and modelling of joint command and control in aircraft maintenance contexts using enterprise models and knowledge graph representations. Int J COMADEM 25:13–22

Zhang Q, Chan FTS, Chung SH, Fu X (2022) Operational aircraft maintenance routing problem incorporating cruise speed control. Eng Optim. https://doi.org/10.1080/0305215X.2022.2146683

Dinis D, Barbosa-Póvoa A, Teixeira ÂP (2019) Valuing data in aircraft maintenance through big data analytics: a probabilistic approach for capacity planning using Bayesian networks. Comput Ind Eng 128:920–936. https://doi.org/10.1016/j.cie.2018.10.015

Eltoukhy AEE, Wang ZX, Chan FTS, Chung SH (2018) Joint optimization using a leader–follower Stackelberg game for coordinated configuration of stochastic operational aircraft maintenance routing and maintenance staffing. Comput Ind Eng 125:46–68. https://doi.org/10.1016/j.cie.2018.08.012

Bouzidi Z, Terrissa LS, Zerhouni N, Ayad S (2020) QoS of cloud prognostic system: application to aircraft engines fleet. Eur J Ind Eng 14:34–57. https://doi.org/10.1504/EJIE.2020.105080

Najjar N, Gupta S, Hare J, Kandil S, Walthall R (2016) Optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems. IEEE Sens J 16(12):4866–4881

Murrieta-Mendoza A, Hamy A, Botez RM (2016) Lateral reference trajectory algorithm using ant colony optimization. In: 16th AIAA aviation technology, integration, and operations conference. American Institute of Aeronautics and Astronautics Inc, AIAA, pp 1–10

Saïd KM, Abdelouahid L (2021) The ibn battouta air traffic control corpus with real life ads-b and metar data. Adv Intell Syst Comput 1193:371–384. https://doi.org/10.1007/978-3-030-51186-9_26

Sui D, Liu K (2023) A framework for optimising flight efficiency of a crossing waypoint by balancing flight conflict frequency and flight-level usage benefits. Aeronaut J. https://doi.org/10.1017/aer.2023.45

Xia B, Mantegh I, Xie W (2021) Integrated emergency self-landing method for autonomous UAS in urban aerial mobility. In: International conference on control, automation and systems. IEEE Computer Society, pp 275–282

Kanyilmaz A, Tichell PRN, Loiacono D (2022) A genetic algorithm tool for conceptual structural design with cost and embodied carbon optimization

Monteiro LB, Ribeiro VF, Garcia CP, Rocha Filho GP, Weigang L (2023) 4D trajectory conflict detection and resolution using decision tree pruning method. IEEE Lat Am Trans 21(2):277–287

Shmelova T, Yatsko M, Sierostanov I (2023) Collaborative decision making (CDM) in emergency caused by captain incapacitation: deterministic and stochastic modelling. Int J Decis Support Syst Technol. https://doi.org/10.4018/IJDSST.320477

Wan L, Ye W, Xu C, Li J, Huang X, Gong W, Tian Y (2023) An approach for three-dimensional sectorization in the terminal area based on airspace function. In: Wireless communications and mobile computing, 2023

Yiu CY, Ng KK, Li X, Zhang X, Li Q, Lam HS, Chong MH (2022) Towards safe and collaborative aerodrome operations: assessing shared situational awareness for adverse weather detection with EEG-enabled Bayesian neural networks. Adv Eng Inform 53:101698

Pérez-Castán JA, Pérez Sanz L, Fernández-Castellano M, Radišić T, Samardžić K, Tukarić I (2022) Learning assurance analysis for further certification process of machine learning techniques: case-study air traffic conflict detection predictor. Sensors 22(19):7680

Ogunsina K, Papamichalis M, DeLaurentis D (2022) Relational dynamic Bayesian network modeling for uncertainty quantification and propagation in airline disruption management. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2022.104846

Ortner P, Steinhöfler R, Leitgeb E, Flühr H (2022) Augmented air traffic control system—artificial intelligence as digital assistance system to predict air traffic conflicts. AI 3:623–644. https://doi.org/10.3390/ai3030036

Liang H, Liu C, Chen K, Kong J, Han Q, Zhao T (2021) Controller fatigue state detection based on ES-DFNN. Aerospace 8(12):383

Tran PN, Pham DT, Goh SK, Alam S, Duong V (2020) An interactive conflict solver for learning air traffic conflict resolutions. J Aerosp Inf Syst 17(6):271–277

Madanan M, Hussain N, Velayudhan NC, Sayed BT (2019) Embedding machine learning in air traffic control systems to generate effective route plans for aircrafts in order to avoid collisions. J Theor Appl Inf Technol 97:605–616

Baklacioglu T (2017) Metaheuristic and machine learning models for TFE-731-2, PW4056, and JT8D-9 Cruise thrust. Int J Turbo Jet Engines 34:221–232. https://doi.org/10.1515/tjj-2016-0002

Wu Y, Sun L, Qu X (2016) A sequencing model for a team of aircraft landing on the carrier. Aerosp Sci Technol 54:72–87. https://doi.org/10.1016/j.ast.2016.04.007

Ogido S, Kyan A, Takazato S, Maesato R, Anezaki T (2016) Proposed integrated drone navigation and autonomous flight system for aerial inspection and surveillance of infrastructure objects. IEEJ Trans Ind Appl 136:753–759. https://doi.org/10.1541/ieejias.136.753

Chen Z, Tang S (2017) Level-of-detail assessment of structural surface damage using spatially sequential stereo images and deep learning methods. In: F-KC, FK (eds) Structural health monitoring 2017: real-time material state awareness and data-driven safety assurance - proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017. DEStech Publications, School of Computing and Engineering, University of Missouri, Kansas City, USA, pp 3210–3216

Durdevic P, Ortiz-Arroyo D, Li S, Yang Z (2019) Vision aided navigation of a quad-rotor for autonomous wind-farm inspection. In: BW, ZK, MD (eds) IFAC-PapersOnLine. Elsevier B.V., Aalborg University, Department of Energy Technology, Niels Bohrs Vej 8, Esbjerg, Denmark, pp 200–205

Korki M, Shankar ND, Shah RN, Waseem SM, Hodges S (2019, February) Automatic fault detection of power lines using unmanned aerial vehicle (UAV). In: 2019 1st International conference on Unmanned Vehicle Systems-Oman (UVS), pp 1–6. IEEE

Yao PF, Geng B, Yang M, Cai YM, Wang T (2020, December) Research on technology of autonomous inspection system for UAV based on improved YOLOv4. In: 2020 5th International conference on Mechanical, Control and Computer Engineering (ICMCCE), pp 664–668. IEEE

Beeram SK, Kadarla S, Kalapatapu P, Pasupuleti VDK (2023) Structural damage identification from video footage using artificial intelligence. In: PR, AM (eds) Lecture notes in civil engineering. Springer Science and Business Media Deutschland GmbH, Mahindra University, Ecole Centrale College of Engineering, Hyderabad, India, pp 774–783

Bécue A, Praça I, Gama J (2021) Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Springer, Dodrecht

Liu C-Y, Chou J-S (2023) Bayesian-optimized deep learning model to segment deterioration patterns underneath bridge decks photographed by unmanned aerial vehicle. Autom Constr. https://doi.org/10.1016/j.autcon.2022.104666

Chen Z, Chen S, Peng X, Bian J, Jiang L, Zhang X (2022, December) ViT-siamese cascade network for transmission image deduplication. In: International forum on digital TV and wireless multimedia communications, pp 391–406. Springer Nature, Singapore

Jung N-J, Hwang M-H, Lee D-H, Song U-K (2022) A study on learning methods for power transmission facilities based on deep learning using multi segmentation and tagging. Trans Korean Inst Electr Eng 71:436–442. https://doi.org/10.5370/KIEE.2022.71.2.436

Viswanath S, Krishnamurthy RJ, Suresh S (2021) Terrain surveillance system with drone and applied machine vision. In: AAD, MS, FPGM (eds) Journal of Physics: Conference Series. IOP Publishing Ltd, Department of Applied Computing Sciences, Madras Sciencitific Research Foundation, Tamil Nadu, Chennai, India

Massaro A, Panarese A, Galiano A (2021) Technological platform for hydrogeological risk computation and water leakage detection based on a convolutional neural network. In: 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., MIUR Research Institute, Dyrecta Lab Srl, BA, Conversano, Italy, pp 225–230

Ghorbani Z, Behzadan AH (2020) Identification and instance segmentation of oil spills using deep neural networks. In: HEN, JB (eds) World Congress on Civil, Structural, and Environmental Engineering. Avestia Publishing, Texas A&M University, College Station, TX, USA, pp 140–141

Wen A (2020) Real-time panoramic multi-target detection based on mobile machine vision and deep learning. In: Journal of Physics: Conference Series. IOP Publishing Ltd, Changchun University of Science and Technology, China

Marathe S (2019) Leveraging drone based imaging technology for pipeline and RoU monitoring survey. In: Society of Petroleum Engineers - SPE Symposium: Asia Pacific Health, Safety, Security, Environment and Social Responsibility 2019. Society of Petroleum Engineers, Cairn Oil and Gas, Vertical of Vedanta Ltd, India

Wu C, Ju B, Wu Y, Lin X, Xiong N, Xu G, Liang X (2019) UAV autonomous target search based on deep reinforcement learning in complex disaster scene. IEEE Access 7:117227–117245

Ivanovas A, Ostreika A, Maskeliūnas R, Damaševičius R, Połap D, Woźniak M (2018) Block matching based obstacle avoidance for unmanned aerial vehicle. In: Artificial intelligence and soft computing: 17th international conference, ICAISC 2018, Zakopane, Poland, June 3–7, 2018, proceedings, Part I 17, pp 58–69. Springer

Sudevan V, Shukla A, Karki H (2018) Inspection of vertical structures in oil and gas industry: a review of current scenario and future trends. In: SEG/AAPG/EAGE/SPE Research and Development Petroleum Conference and Exhibition 2018, RDP 2018. Society of Exploration Geophysicists, Department of Mechanical Engineering, Petroleum Institute, A part of Khalifa University of Science and Technology, United Arab Emirates, pp 65–68

Stuffler T, Mosebach H, Kampf D, Honne A, Odegard H, Schumann-Olsen H, Tan G (2007) The air quality monitor ANITA-going into operation on the international space station (No. 2007-01-3148). SAE technical paper

Mc Farland MJ, Nelson TM, Palmer GR (2004) Development of a hazardous air pollutants monitoring program using the data quality objectives process. J Air Waste Manag Assoc 54:614–622. https://doi.org/10.1080/10473289.2004.10470932

Belsma LO (2004) Satellite aerosol detection in the NPOESS era. In: Regional and global perspectives on haze. Aerospace Corporation, Los Angeles, CA, USA, pp 991–1010

Martinelli E, Zampetti E, Pantalei S, Lo Castro F, Santonico M, Pennazza G, Cotronei V (2007) Design and test of an electronic nose for monitoring the air quality in the international space station. Microgravity Sci Technol 19:60–64

Mashkov O, Kosenko V, Savina N, Rozov Y, Radetska S, Voronenko M (2020) Information technologies for environmental monitoring of plankton algae distribution based on satellite image data. In: Lecture notes in computational intelligence and decision making: proceedings of the XV international scientific conference “Intellectual Systems of Decision Making and Problems of Computational Intelligence” (ISDMCI'2019), Ukraine, May 21–25, 2019, pp 434–446. Springer

Rohi G, Ejofodomi O, Ofualagba G (2020) Autonomous monitoring, analysis, and countering of air pollution using environmental drones. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e03252

Basit A, Ghauri BM, Qureshi MA (2019) Estimation of ground level pm2.5 by using modis satellite data. In: 6th International conference on Aerospace Science and Engineering, ICASE 2019. Institute of Electrical and Electronics Engineers Inc., National Center for Remote Sensing Geo-Informatics, Institute of Space Technology, Karachi, Pakistan

Berlowitz I (2018) Commercial transport aircraft cabin clean air using bleed air system. In: 58th Israel Annual Conference on Aerospace Sciences, IACAS 2018. Israel Annual Conference on Aerospace Sciences, Israel Aerospace Industries, BEDEK Aviation Group, Aircraft and Programs Division, Israel, pp 149–196

Yan D, Wang C, Zhou N, You X (2018) Construction and application of ocean big data platform based on UAV system. In: LW, HY, ZZ et al (eds) Proceedings of SPIE - The International Society for Optical Engineering. SPIE, CH UAV Technology Co., Ltd, China

Ionel I, Popescu F (2009) Data acquisition system in a mobile air quality monitoring station. In: Proceedings - 2009 5th international Symposium on Applied Computational Intelligence and Informatics, SACI 2009. University Politehnica of Timisoara, Faculty of Mechanical Engineering, MMUT Department, Timisoara, Romania, pp 557–562

Schwabacher M, Gelsey A (1998) Multilevel simulation and numerical optimization of complex engineering designs. J Aircr 35:387–397. https://doi.org/10.2514/2.2336

Giannakoglou KC (1999) Designing turbomachinery blades using evolutionary methods. In: Proceedings of the ASME Turbo Expo

Tüű-Szabó B, Földesi P, Kóczy LT (2017) Improved discrete bacterial memetic evolutionary algorithm for the traveling salesman problem. In: Advances in intelligent systems and computing. pp 27–38

Murrieta-Mendoza A, Hamy A, Botez RM (2017) Four- and three-Dimensional aircraft reference trajectory optimization inspired by ant colony optimization. J Aerosp Inf Syst 14:597–616. https://doi.org/10.2514/1.I010540

Guraksin AM, Ozcan A (2023) ACO-based approach for integrating product lifecycle management with MRO services in aviation industry. Soft Comput 27:337–361. https://doi.org/10.1007/s00500-022-07560-4

Dang HN, Chang K, Chen G, Chen HM, Khan S, Franco M, Blasch E (2023, June) Scheduling condition-based maintenance: an explainable deep reinforcement learning approach via reward decomposition. In: 2023 26th International conference on Information Fusion (FUSION), pp 1–8. IEEE

Yang W, Wu M, Wen X, Wang S, Heng Y, Zhang Z (2022, November) Distributed task architecture of UAV swarm based on potential field direction. In: International conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), Vol 12340, pp 412–416. SPIE

Domínguez-Monferrer C, Fernández-Pérez J, De Santos R, Miguélez MH, Cantero JL (2022) CFRP drilling process control based on spindle power consumption from real production data in the aircraft industry. Proc CIRP 107:1533–1538

Abouzeid AA, Eldin MM, Razek MA (2021) Particle swarm optimization for airlines fleet assignment. Indones J Electr Eng Comput Sci 22:427–434. https://doi.org/10.11591/ijeecs.v22.i1.pp427-434

Li S, Ma C, Li Q, Zeng J, Wang L (2020, November) Application of improved ant colony algorithm in flight path planning. In: 2020 IEEE international conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Vol 1, pp 763–771. IEEE.

Yang L, Wang J, Zhang G, Li X, Fu H (2019, June) An adaptive fault diagnosis system framework for aircraft based on man-in-loop. In: 2019 IEEE International conference on Prognostics and Health Management (ICPHM), pp 1–4. IEEE

Chan FTS, Eltoukhy AEE (2018) Investigating the interrelationship between stochastic aircraft routing of airlines and maintenance staffing of maintenance providers. In: 2018 5th International conference on Industrial Engineering and Applications, ICIEA 2018. pp 254–261

Oktay T, Arik S, Turkmen I, Uzun M, Celik H (2018) Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio. Aircr Eng Aerosp Technol 90(8):1203–1212

Sousa H, Teixeira R, Cardoso HL, Oliveira E (2015) Airline disruption management: dynamic aircraft scheduling with ant colony optimization. In: ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, proceedings. pp 398–405

Cruciol LLBV, Weigang L, Li L, Clarke J-P (2014) In-flight cost optimization for air traffic flow management using data mining method on big data. In: OPT-i 2014 - 1st international conference on engineering and applied sciences optimization, proceedings. pp 1491–1498

Fu L, Liu J, Meng G, Xie F (2013) Research on beyond visual range target allocation and multi-aircraft collaborative decision-making. In: 2013 25th Chinese Control and Decision Conference, CCDC 2013. pp 586–590

Kayaalp K, Metlek S, Ekici S, Şöhret Y (2021) Developing a model for prediction of the combustion performance and emissions of a turboprop engine using the long short-term memory method. Fuel. https://doi.org/10.1016/j.fuel.2021.121202

Baumann S, Klingauf U (2020) Modeling of aircraft fuel consumption using machine learning algorithms. CEAS Aeronaut J 11:277–287. https://doi.org/10.1007/s13272-019-00422-0

Ayhan S, Samet H (2016) Aircraft trajectory prediction made easy with predictive analytics. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. pp 21–30

Zhang T, Ding M, Zuo H, Zeng L, Sun Z (2016, June) A two-stage speed profile design methodology for smooth and fuel efficient aircraft ground movement. In: 2016 12th IEEE International Conference on Control and Automation (ICCA), pp 479–484. IEEE

Morris R, Chang ML, Archer R, Cross EV, Thompson S, Franke J, Hemann G (2015, April) Self-driving aircraft towing vehicles: a preliminary report. In: Workshops at the twenty-ninth AAAI conference on artificial intelligence

Yairi T, Takeishi N, Oda T, Nakajima Y, Nishimura N, Takata N (2017) A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Trans Aerosp Electron Syst 53(3):1384–1401

Aksit E, Haapala KR, Tabei A (2023) Digital multiphase material microstructures for image-based AI methods. In: Lecture notes in mechanical engineering. School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA, pp 723–734

Ahmad A (2019) Reliable and fault tolerant systems on chip through design for testability. In: Proceedings - 2019 Amity International conference on Artificial Intelligence, AICAI 2019. Department of Electrical Computer Engineering, College of Engineering, Sultan Qaboos University, Oman, Oman, pp 50–53

Wang M, Shi D, Guan N, Zhang T, Wang L, Li R (2019, November) Unsupervised pedestrian trajectory prediction with graph neural networks. In: 2019 IEEE 31st International conference on Tools with Artificial Intelligence (ICTAI). pp 832–839. IEEE

Ye B, Sherry L, Chen C-H, Tian Y (2016) Comparison of alternative route selection strategies based on simulation optimization. Chin J Aeronaut 29:1749–1761. https://doi.org/10.1016/j.cja.2016.09.012

Hu Q, Shao X, Yang H, Duan C (2022) Spacecraft attitude planning and control under multiple constraints: review and prospects. Hangkong Xuebao. https://doi.org/10.7527/S1000-6893.2022.27351

Suh YA, Kim JH, Yim M-S (2019) An investigation into the feasibility of monitoring a worker’s psychological distress. In: Advances in intelligent systems and computing. Nuclear Environment and Nuclear Security Laboratory, Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, pp 476–487

Thukaram P, Mohan S (2019) Digital twins for prognostic profiling. SAE Tech Pap. https://doi.org/10.4271/2019-28-2456

Aslan D, Çetin BB, Özbilgin İG (2019) An innovative technology: augmented reality based information systems. In: SS (ed) Procedia Computer Science. Elsevier B.V., HAVELSAN Inc., Ankara, Turkey, pp 407–414

Khan A (2023) Implementation of industry 4.0 in the development of the space industry

Iqbal BA, Yadav A (2021) Fourth industrial revolution: its role and contribution in employment, generation and skills development. J Glob Econ Trade Int Bus 1:85–96

Van Roy V, Vertesy D, Damioli G (2020) AI and robotics innovation Handb labor. Hum Resour Popul Econ 1:1–35

Emanuilov I, Dheu O (2021) Flying high for AI? Perspectives on EASA’s roadmap for AI in aviation. Air Sp Law 46:1

Mandrake L, Doran G, Goel A, Ono H, Amini R, Feather MS, Kaufman J (2022, March) Space applications of a trusted ai framework: experiences and lessons learned. In: 2022 IEEE Aerospace Conference (AERO), pp 1–20. IEEE

Hallows R, Glazier L, Katz MS, Aznar M, Williams M (2022) Safe and ethical artificial intelligence in radiotherapy–lessons learned from the aviation industry. Clin Oncol 34(2):99–101

Koch W (2023) AI for aerospace and electronic systems: technical dimensions of responsible design. IEEE Aerosp Electron Syst Mag 38:106–111. https://doi.org/10.1109/MAES.2022.3228300

Aggour KS, Gupta VK, Ruscitto D, Ajdelsztajn L, Bian X, Brosnan KH, Vinciquerra J (2019) Artificial intelligence/machine learning in manufacturing and inspection: a GE perspective. MRS Bull 44(7):545–558

Savić D (2022) Digital water developments and lessons learned from automation in the car and aircraft industries. Engineering 9:35–41

Yin Y, He J, Zhao L, Pei J, Yang X, Sun Y, Chen Q (2022) Identification of key volatile organic compounds in aircraft cabins and associated inhalation health risks. Environ Int 158:106999

Dong Y, Tao J, Zhang Y, Lin W, Ai J (2021) Deep learning in aircraft design, dynamics, and control: review and prospects. IEEE Trans Aerosp Electron Syst 57(4):2346–2368

Oche PA, Ewa GA, Ibekwe N (2021) Applications and challenges of artificial intelligence in space missions. IEEE Access

Furano G, Meoni G, Dunne A, Moloney D, Ferlet-Cavrois V, Tavoularis A, Fanucci L (2020) Towards the use of artificial intelligence on the edge in space systems: challenges and opportunities. IEEE Aerosp Electron Syst Mag 35(12):44–56

Mirchandani S, Adhikari S (2020) Aerospace cybersecurity threat vector assessment. In: ASCEND 2020. p 4116

Kharchenko V, Illiashenko O, Fesenko H, Babeshko I (2022) AI cybersecurity assurance for autonomous transport systems: scenario, model, and IMECA-based analysis. International conference on multimedia communications, services and security. Springer, Berlin, pp 66–79

Chapter   Google Scholar  

Breda P, Markova R, Abdin A, Jha D, Carlo A, Mantı NP (2022, September) Cyber vulnerabilities and risks of AI technologies in space applications. In: 73rd International Astronautical Congress (IAC), Paris, France

Ali M, Hu YF, Luong DK, Oguntala G, Li JP, Abdo K (2020, October) Adversarial attacks on ai based intrusion detection system for heterogeneous wireless communications networks. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), pp 1–6. IEEE

Cockburn IM, Henderson R, Stern S (2018) The impact of artificial intelligence on innovation: an exploratory analysis. The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 115–146

Ezhilarasu CM, Skaf Z, Jennions IK (2019) The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): challenges and opportunities. Prog Aerosp Sci 105:60–73. https://doi.org/10.1016/j.paerosci.2019.01.001

Martin A-S, Freeland S (2021) The advent of artificial intelligence in space activities: new legal challenges. Space Policy 55:101408

Johnson J (2019) Artificial intelligence & future warfare: implications for international security. Def Secur Anal 35:147–169

Tang Y-C (2009) An approach to budget allocation for an aerospace company-Fuzzy analytic hierarchy process and artificial neural network. Neurocomputing 72:3477–3489. https://doi.org/10.1016/j.neucom.2009.03.020

Battina DS (2018) The future of artificial intelligence at work: a review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Int J Innov Eng Res Technol 5:1

Dou X (2020) Big data and smart aviation information management system. Cogent Bus Manag 7:1766736

Kania EB (2019) Chinese military innovation in artificial intelligence. Testimony to US-China Econ Secur Rev Comm

Dhanabalan T, Sathish A (2018) Transforming Indian industries through artificial intelligence and robotics in industry 4.a0. Int J Mech Eng Technol 9:835–845

Gill AS (2019) Artificial intelligence and international security: the long view. Ethics Int Aff 33:169–179

De Spiegeleire S, Maas M, Sweijs T (2017) Artificial intelligence and the future of defense: strategic implications for small-and medium-sized force providers. The Hague Centre for Strategic Studies

Morgan FE, Boudreaux B, Lohn AJ, Ashby M, Curriden C, Klima K, Grossman D (2020) Military applications of artificial intelligence. RAND Corporation, Santa Monica

Babuta A, Oswald M, Janjeva A (2020) Artificial intelligence and UK national security: policy considerations

Schmidt E, Work RO, Bajraktari Y, Catz S, Horvitz EJ, Chien S, Moore AW (2021) National Security Commission on artificial intelligence

Allen GC (2019) Understanding China’s AI strategy: clues to Chinese strategic thinking on artificial intelligence and national security

Hoadley DS, Lucas NJ (2018) Artificial intelligence and national security

Mudgal S, Li H, Rekatsinas T, Doan A, Park Y, Krishnan G, Raghavendra V (2018, May) Deep learning for entity matching: a design space exploration. In: Proceedings of the 2018 international conference on management of data, pp 19–34

Lamperti F, Roventini A, Sani A (2018) Agent-based model calibration using machine learning surrogates. J Econ Dyn Control 90:366–389

Afshinnekoo E, Scott RT, MacKay MJ, Pariset E, Cekanaviciute E, Barker R, Beheshti A (2020) Fundamental biological features of spaceflight: advancing the field to enable deep-space exploration. Cell 183(5):1162–1184

Gunning D, Aha D (2019) DARPA’s explainable artificial intelligence (XAI) program. AI Mag 40:44–58

Lu Y (2019) Artificial intelligence: a survey on evolution, models, applications and future trends. J Manag Anal 6:1–29

Li BH, Hou BC, Yu WT, Lu XB, Yang CW (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering 18(1):86–96

Bughin J, Hazan E, Sree Ramaswamy P, Dc, W, Chu M (2017) Artificial intelligence the next digital frontier

McGrew T, Sysoeva V, Cheng CH, Miller C, Scofield J, Scott MJ (2022) Condition monitoring of DC-link capacitors using time–frequency analysis and machine learning classification of conducted EMI. IEEE Trans Power Electron 37(10):12606–12618

Varian H (2018) Artificial intelligence, economics, and industrial organization. the economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 399–419

Soni N, Sharma EK, Singh N, Kapoor A (2020) Artificial intelligence in business: from research and innovation to market deployment. Proc Comput Sci 167:2200–2210

Makridakis S (2017) The forthcoming Artificial Intelligence (AI) revolution: its impact on society and firms. Futures 90:46–60

Irwan D, Ali M, Ahmed AN, Jacky G, Nurhakim A, Ping Han MC, El-Shafie A (2023) Predicting water quality with artificial intelligence: a review of methods and applications. Arch Comput Methods Eng 30(8):4633–4652

Download references

Author information

Authors and affiliations.

School of Mechanical Engineering, Lovely Professional University, Phagwara, India

Kamal Hassan

Department of Aerospace Engineering, Lovely Professional University, Phagwara, India

Amit Kumar Thakur

Department of Industrial and Production, Dr B R Ambedkar National Institute of Technology, Jalandhar, India

Gurraj Singh

School of Civil Engineering, Lovely Professional University, Phagwara, India

Jaspreet Singh

Lovely Faculty of Technology and Science, Lovely Professional University, Phagwara, India

Lovi Raj Gupta

Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India

Rajesh Singh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Amit Kumar Thakur .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Hassan, K., Thakur, A.K., Singh, G. et al. Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review. Arch Computat Methods Eng 31 , 4031–4086 (2024). https://doi.org/10.1007/s11831-024-10105-7

Download citation

Received : 09 November 2023

Accepted : 05 March 2024

Published : 16 April 2024

Issue Date : September 2024

DOI : https://doi.org/10.1007/s11831-024-10105-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research