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Physics-Informed Machine Learning : An Emerging Trend in Tribology

Title data

Marian, Max ; Tremmel, Stephan:
Physics-Informed Machine Learning : An Emerging Trend in Tribology.
In: Lubricants. Vol. 11 (2023) Issue 11 . - 463.
ISSN 2075-4442

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: artificial intelligence; machine learning; tribo-informatics; physics-informed neural network; friction; wear; lubrication
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Engineering Design and CAD > Chair Engineering Design and CAD - Univ.-Prof. Dr.-Ing Stephan Tremmel
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 16 Jan 2024 07:15
Last Modified: 16 Jan 2024 07:15