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

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

Authors

Zhu, SP (Author) Other; Abilio M P De Jesus (Author) FEUP; Berto, F (Author) Other; Michopoulos, JG (Author) Other; Iacoviello, 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 - 0 Citations

Other information

Authenticus ID: P-00Z-3YX

DOI: 10.1098/rsta.2023.0176

Abstract (EN): 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)'.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3