IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-031-71495-5_2.html
   My bibliography  Save this book chapter

Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective

In: Artificial Intelligence for Safety and Reliability Engineering

Author

Listed:
  • Dac Hieu Nguyen

    (Thuyloi University
    Dong A University)

  • Thi Hien Nguyen

    (CY Cergy Paris Université)

  • Kim Duc Tran

    (Dong A University)

  • Kim Phuc Tran

    (Dong A University
    ENSAIT, University of Lille)

Abstract

The convergence of physics-informed and machine learning has led to the emergence of Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability and safety of complex industrial systems. Traditional methods in reliability engineering often rely on physics-based models, which, despite their robustness, face limitations when dealing with highly complex systems involving multiple interacting scales and incomplete knowledge. PIML addresses these challenges by integrating physical laws directly into machine learning models, combining the strengths of data-driven and physics-based approaches. This integration significantly enhances the accuracy and interpretability of predictions, which is crucial for industrial systems’ reliability and safety. Inspired by this motivation, this chapter delves into the state-of-the-art developments in PIML, highlighting its applications, benefits, and challenges within reliability and safety engineering.

Suggested Citation

  • Dac Hieu Nguyen & Thi Hien Nguyen & Kim Duc Tran & Kim Phuc Tran, 2024. "Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Artificial Intelligence for Safety and Reliability Engineering, pages 5-23, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-71495-5_2
    DOI: 10.1007/978-3-031-71495-5_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ssrchp:978-3-031-71495-5_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.