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

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

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

Afiliação

Zhu SP; School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
De Jesus AMP; INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal.
Berto F; Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Roma, Italy.
Michopoulos JG; Computational Multiphysics Systems Lab, Center for Materials Physics and Technology, Naval Research Laboratory, USA.
Iacoviello F; Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy.
Wang Q; MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, People's Republic of China.

Philos Trans A Math Phys Eng Sci ; 381(2260): 20230176, 2023 Nov 13.

Article em En | MEDLINE | ID: mdl-37742706

Palavras-chave

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

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Philos Trans A Math Phys Eng Sci Assunto da revista: BIOFISICA / ENGENHARIA BIOMEDICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google