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Physics-informed machine learning in intelligent manufacturing: a review.

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Τίτλος: Physics-informed machine learning in intelligent manufacturing: a review.
Συγγραφείς: Leng, Jiewu, Zuo, Kaiwen, Xu, Caiyu, Zhou, Xueliang, Zheng, Shuai, Kang, Jiawen, Liu, Qiang, Chen, Xin, Shen, Weiming, Wang, Lihui, Gao, Robert X.
Πηγή: Journal of Intelligent Manufacturing; Jun2026, Vol. 37 Issue 6, p2215-2257, 43p
Θεματικοί όροι: Constraints (Physics), Physics, Artificial neural networks, Machine learning, Simulation methods & models, Manufacturing process automation
Περίληψη: Machine learning stands as a potent solution within the intelligent manufacturing sector. However, the conventional training of deep neural networks typically demands extensive datasets, which can be challenging to compile, particularly in various engineering contexts. Physics-Informed Machine Learning (PIML) offers a solution to this challenge by integrating prior knowledge and physical laws to direct model training, thereby augmenting accuracy, interpretability, robustness, and generalization capabilities. Physics-Informed Neural Networks (PINNs), as a model prominent within the PIML landscape, have gained widespread adoption across intelligent manufacturing applications. This paper provides a comprehensive review of the current research on PIML and PINNs, especially in the intelligent manufacturing sector. The analysis is structured around four key dimensions: (1) The methods of physical constraint implementation in PIML; (2) The modeling techniques employed by PINNs; (3) The training methodologies for PINNs; and (4) The industrial physics and potential embedding methods. The paper also outlines existing challenges and potential future research directions in PIML-driven intelligent manufacturing. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s10845-025-02641-1
    Name: EDS - Springer Nature Journals (s7799221)
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  Data: Physics-informed machine learning in intelligent manufacturing: a review.
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  Data: <searchLink fieldCode="AR" term="%22Leng%2C+Jiewu%22">Leng, Jiewu</searchLink><br /><searchLink fieldCode="AR" term="%22Zuo%2C+Kaiwen%22">Zuo, Kaiwen</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Caiyu%22">Xu, Caiyu</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Xueliang%22">Zhou, Xueliang</searchLink><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Shuai%22">Zheng, Shuai</searchLink><br /><searchLink fieldCode="AR" term="%22Kang%2C+Jiawen%22">Kang, Jiawen</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qiang%22">Liu, Qiang</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xin%22">Chen, Xin</searchLink><br /><searchLink fieldCode="AR" term="%22Shen%2C+Weiming%22">Shen, Weiming</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Lihui%22">Wang, Lihui</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Robert+X%2E%22">Gao, Robert X.</searchLink>
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  Data: Journal of Intelligent Manufacturing; Jun2026, Vol. 37 Issue 6, p2215-2257, 43p
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  Data: <searchLink fieldCode="DE" term="%22Constraints+%28Physics%29%22">Constraints (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink>
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  Data: Machine learning stands as a potent solution within the intelligent manufacturing sector. However, the conventional training of deep neural networks typically demands extensive datasets, which can be challenging to compile, particularly in various engineering contexts. Physics-Informed Machine Learning (PIML) offers a solution to this challenge by integrating prior knowledge and physical laws to direct model training, thereby augmenting accuracy, interpretability, robustness, and generalization capabilities. Physics-Informed Neural Networks (PINNs), as a model prominent within the PIML landscape, have gained widespread adoption across intelligent manufacturing applications. This paper provides a comprehensive review of the current research on PIML and PINNs, especially in the intelligent manufacturing sector. The analysis is structured around four key dimensions: (1) The methods of physical constraint implementation in PIML; (2) The modeling techniques employed by PINNs; (3) The training methodologies for PINNs; and (4) The industrial physics and potential embedding methods. The paper also outlines existing challenges and potential future research directions in PIML-driven intelligent manufacturing. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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