Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

The computerized simulations of physical and socio-economic systems have proliferated in the past decade, at the same time, the capability to develop high-fidelity system predictive models is of growing importance for a multitude of reliability and system safety applications. Traditionally, methodol...

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Bibliographic Details

Published in:	Reliability engineering & system safety Vol. 230; p. 108900
Main Authors:	Xu, Yanwen, Kohtz, Sara, Boakye, Jessica, Gardoni, Paolo, Wang, Pingfeng
Format:	Journal Article
Language:	English
Published:	Barking Elsevier BV 01.02.2023
Subjects:	Hybrid systems Learning algorithms Machine learning Modelling Motivation Physics Prediction models Reliability engineering Safety State-of-the-art reviews System effectiveness System reliability
ISSN:	0951-8320, 1879-0836
Online Access:	Get full text
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