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Marian, Max ; Tremmel, Stephan:
Physics-Informed Machine Learning : An Emerging Trend in Tribology.
In: Lubricants.
Bd. 11
(2023)
Heft 11
.
- 463.
ISSN 2075-4442
DOI: https://doi.org/10.3390/lubricants11110463
Abstract
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.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | artificial intelligence; machine learning; tribo-informatics; physics-informed neural network; friction; wear; lubrication |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Konstruktionslehre und CAD > Lehrstuhl Konstruktionslehre und CAD - Univ.-Prof. Dr.-Ing. Stephan Tremmel |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 16 Jan 2024 07:15 |
| Letzte Änderung: | 16 Jan 2024 07:15 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/88231 |