Physics-Informed Machine Learning—An Emerging Trend in Tribology
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/11/11/463 |
| _version_ | 1797458640435675136 |
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| author | Max Marian Stephan Tremmel |
| author_facet | Max Marian Stephan Tremmel |
| author_sort | Max Marian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:40:04Z |
| format | Article |
| id | doaj.art-5dfc9f4e29a347829d50a3c3228efaf2 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-09T16:40:04Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-5dfc9f4e29a347829d50a3c3228efaf22023-11-24T14:52:45ZengMDPI AGLubricants2075-44422023-10-01111146310.3390/lubricants11110463Physics-Informed Machine Learning—An Emerging Trend in TribologyMax Marian0Stephan Tremmel1Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul 6904411, ChileEngineering Design and CAD, University of Bayreuth, Universitätsstr. 30, 95447 Bayreuth, GermanyPhysics-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.https://www.mdpi.com/2075-4442/11/11/463artificial intelligencemachine learningtribo-informaticsphysics-informed neural networkfrictionwear |
| spellingShingle | Max Marian Stephan Tremmel Physics-Informed Machine Learning—An Emerging Trend in Tribology Lubricants artificial intelligence machine learning tribo-informatics physics-informed neural network friction wear |
| title | Physics-Informed Machine Learning—An Emerging Trend in Tribology |
| title_full | Physics-Informed Machine Learning—An Emerging Trend in Tribology |
| title_fullStr | Physics-Informed Machine Learning—An Emerging Trend in Tribology |
| title_full_unstemmed | Physics-Informed Machine Learning—An Emerging Trend in Tribology |
| title_short | Physics-Informed Machine Learning—An Emerging Trend in Tribology |
| title_sort | physics informed machine learning an emerging trend in tribology |
| topic | artificial intelligence machine learning tribo-informatics physics-informed neural network friction wear |
| url | https://www.mdpi.com/2075-4442/11/11/463 |
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