Preface to the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'

Shun-Peng Zhu; Abílio M P De Jesus; Filippo Berto; John G Michopoulos; Francesco Iacoviello; Qingyuan Wang

doi:10.1098/rsta.2023.0248

Preface to the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'

Philos Trans A Math Phys Eng Sci. 2024 Jan 8;382(2264):20230248. doi: 10.1098/rsta.2023.0248. Epub 2023 Nov 20.

Authors

Shun-Peng Zhu¹, Abílio M P De Jesus², Filippo Berto³, John G Michopoulos⁴, Francesco Iacoviello⁵, Qingyuan Wang^{6

7}

Affiliations

¹ School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
² INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal.
³ Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Roma, Italy.
⁴ Computational Multiphysics Systems Lab, Center for Materials Physics and Technology, Naval Research Laboratory, Washington, District of Columbia, USA.
⁵ Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy.
⁶ MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, People's Republic of China.
⁷ Advanced Research Institute, Chengdu University, Chengdu 610106, People's Republic of China.

Abstract

As an emerging research field, physics-informed machine learning and its structural integrity applications may bring new opportunities to the intelligent solution of engineering problems. Pure data-driven approaches have some limitations when solving engineering problems due to lack of interpretability and data hungry applications. Therefore, further unlocking the potential of machine learning will be an important research direction in the future. Knowledge-driven machine learning methods may have a profound impact on future engineering research. The theme of this special issue focuses on more specific physics-informed machine learning methods and case studies. This issue presents a series of practical ideas to demonstrate the huge potential of physics-informed machine learning for solving engineering problems with high precision and efficiency. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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