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. 2023 Nov 13;381(2260):20230176.
doi: 10.1098/rsta.2023.0176. Epub 2023 Sep 25.

Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'

Shun-Peng Zhu  1 , Abílio M P De Jesus  2 , Filippo Berto  3 , John G Michopoulos  4 , Francesco Iacoviello  5 , Qingyuan Wang  6   7
Affiliations

Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'

Shun-Peng Zhu et al. Philos Trans A Math Phys Eng Sci. .

Abstract

The issue focuses on physics-informed machine learning and its applications for structural integrity and safety assessment of engineering systems/facilities. Data science and data mining are fields in fast development with a high potential in several engineering research communities; in particular, advances in machine learning (ML) are undoubtedly enabling significant breakthroughs. However, purely ML models do not necessarily carry physical meaning, nor do they generalize well to scenarios on which they have not been trained on. This is an emerging field of research that potentially will raise a huge impact in the future for designing new materials and structures, and then for their proper final assessment. This issue aims to update the current research state of the art, incorporating physics into ML models, and providing tools when dealing with material science, fatigue and fracture, including new and sophisticated algorithms based on ML techniques to treat data in real-time with high accuracy and productivity. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Keywords: failure mechanism modelling; machine learning; physics-informed machine learning; prognostic and health management; structural integrity.

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Conflict of interest statement

This theme issue was put together by the Guest Editor team under supervision from the journal's Editorial staff, following the Royal Society's ethical codes and best-practice guidelines. The Guest Editor team invited contributions and handled the review process. Individual Guest Editors were not involved in assessing papers where they had a personal, professional or financial conflict of interest with the authors or the research described. Independent reviewers assessed all papers. Invitation to contribute did not guarantee inclusion.

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