Artificial Intelligence in Mechanical Engineering: From Statistical Learning to Generative Models

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

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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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

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