Physics-Informed Machine Learning—An Emerging Trend in Tribology

Simple item page
dc.article.number463
dc.catalogadorgrr
dc.contributor.authorMarian, Max
dc.contributor.authorTremmel, Stephan
dc.date.accessioned2024-05-28T21:31:07Z
dc.date.available2024-05-28T21:31:07Z
dc.date.issued2023
dc.description.abstract© 2023 by the authors.Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods.
dc.description.funderEuropean Regional Development Fund in Bavaria
dc.fechaingreso.objetodigital2024-05-28
dc.fuente.origenScopus
dc.identifier.doi10.3390/lubricants11110463
dc.identifier.issn20754442
dc.identifier.scopusidSCOPUS_ID:85178090209
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85938
dc.information.autorucEscuela de Ingeniería; Marian, Max; 0000-0003-2045-6649; 1247429
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.revistaLubricants
dc.rightsacceso abierto
dc.subjectartificial intelligence
dc.subjectfriction
dc.subjectlubrication
dc.subjectmachine learning
dc.subjectphysics-informed neural network
dc.subjecttribo-informatics
dc.subjectwear
dc.titlePhysics-Informed Machine Learning—An Emerging Trend in Tribology
dc.typeartículo
dc.volumen11
sipa.codpersvinculados1247429
sipa.trazabilidadSCOPUS;2023-12-10
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
lubricants-11-00463.pdf
Size:
7.9 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas