Artificial Intelligence in Mechanical Engineering: From Statistical Learning to Generative Models
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 August 2025 | Viewed by 3671
Special Issue Editors
Interests: intelligent fault diagnosis; condition monitoring; predictive maintenance; rolling bearings; generative AI; machine learning; deep learning; transfer learning; rotating machinery; generative adversarial networks; explainable AI; machine design; artificial intelligence; image processing
Interests: rotordynamics; fatigue; damage estimation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Special Issue “Artificial Intelligence in Mechanical Engineering: From Statistical Learning to Generative Models” covers the advancements in artificial intelligence in the field of mechanical engineering, tracing its development from statistical learning through discriminative and regression models to generative models. This Special Issue encompasses a broad spectrum of AI applications, such as robotics, automation, predictive maintenance, optimization of manufacturing processes, advanced materials design, machine design, structural integrity, damage identification, and evolution and fatigue life estimation. The broad array of subjects exemplifies the far-reaching influence of AI in various aspects of the field, showcasing its adaptability in addressing a wide range of multifaceted problems.
We welcome submissions that explore the application and development of machine learning, deep learning, transfer learning, and generative artificial intelligence approaches. Articles should address algorithmic approaches that are based on data, improving our comprehension in the given domains.
We also encourage papers that analyze scenarios with limited, missing, or heavily contaminated data that necessitate extensive cleaning and preprocessing. We highly appreciate entries that showcase innovative AI solutions for addressing these prevalent data-related obstacles, showcasing the adaptability and robustness of AI technology in practical applications.
We welcome scholars and professionals to submit their most recent research discoveries and assessments, which not only advance the boundaries of current technology but also provide practical solutions and insights into the future trajectory of artificial intelligence in mechanical engineering. Review papers are welcome as well.
Dr. Luigi Gianpio Di Maggio
Prof. Dr. Cristiana Delprete
Guest Editors
Manuscript Submission Information
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Keywords
artificial intelligence
mechanical engineering
statistical learning
intelligent fault diagnosis
robotics
machine learning
machine design
material design
fatigue damage
deep learning
generative AI
manufacturing
structural health monitoring
structural integrity
industry
rotating machinery
feature extraction
data-driven mechanics
vibration
sensors
data analysis
classification
regression
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