Machine Learning for Noise and Vibration Engineering

Dear Colleagues,

Machine learning techniques offer new pathways in noise and vibration engineering. New modeling techniques emerge that make use of large data sets, pattern recognition deepens the insight into noise and vibration phenomena, and new algorithms can “learn” to identify the dynamic properties of mechanical structures. The Special Issue “Machine Learning for Noise and Vibration Engineering” aims at reporting on machine learning techniques in noise and vibration engineering. Authors are invited to submit their original work, including but not limited to:

• Artificial neural networks, deep learning including statistical analyses and explainability techniques;
• Data-driven vibration and acoustic modeling techniques;
• Inverse problems in vibration and acoustics based on machine learning;
• engineering noise control by means of data-driven approaches;
• Pattern recognition and pattern prediction including applications in vibration and acoustic-based condition monitoring;
• Identifying, quantifying, and controlling uncertainty of data-driven models.

Dr. Christian Adams
Dr. Merten Stender
Prof. Dr. Tyler Dare
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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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