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

被引：0
|
作者：
Zhu, Shun-Peng [1 ]
De Jesus, Abilio M. P. [2 ]
Berto, Filippo [3 ]
Michopoulos, John G. [4 ]
Iacoviello, Francesco [5 ]
Wang, Qingyuan [6 ,7 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China
[2] Univ Porto, Fac Engn, INEGI, P-4200465 Porto, Portugal
[3] Sapienza Univ Rome, Dept Chem Engn Mat & Environm, I-00184 Rome, Italy
[4] Naval Res Lab, Ctr Mat Phys & Technol, Computat Multiphys Syst Lab, Washington, DC USA
[5] Univ Cassino & Southern Lazio, Dept Civil & Mech Engn, Cassino, Italy
[6] Sichuan Univ, Coll Architecture & Environm, MOE Key Lab Deep Earth Sci & Engn, Chengdu 610065, Peoples R China
[7] Chengdu Univ, Adv Res Inst, Chengdu 610106, Peoples R China
来源：
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2024年 / 382卷 / 2264期
基金：
中国国家自然科学基金;
关键词：
machine learning; physics-informed machine learning; structural integrity; failure mechanism modelling; prognostic and health management;
D O I：
10.1098/rsta.2023.0248
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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)'.
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页数：2
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