Physics-informed machine learning and its structural integrity applications: state of the art

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Zhu, SP (Author) Other; Wang, LY (Author) Other; Luo, CQ (Author) Other; Correia, JAFO (Author) Other; Abilio M P De Jesus (Author) FEUP; Berto, F (Author) Other; Wang, QY (Author) Other

Journal

Title: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Vol. 381

ISSN: 1364-503X

Publisher: The Royal Society

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-010-8PF

DOI: 10.1098/rsta.2022.0406

Abstract (EN): The development of machine learning (ML) provides a promising solution to guarantee the structural integrity of critical components during service period. However, considering the lack of respect for the underlying physical laws, the data hungry nature and poor extrapolation performance, the further application of pure data-driven methods in structural integrity is challenged. An emerging ML paradigm, physics-informed machine learning (PIML), attempts to overcome these limitations by embedding physical information into ML models. This paper discusses different ways of embedding physical information into ML and reviews the developments of PIML in structural integrity including failure mechanism modelling and prognostic and health management (PHM). The exploration of the application of PIML to structural integrity demonstrates the potential of PIML for improving consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reducing dependence on training data. The analysis and findings of this work outline the limitations at this stage and provide some potential research direction of PIML to develop advanced PIML for ensuring structural integrity of engineering systems/facilities.This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15