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Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications'
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,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518223/ https://www.ncbi.nlm.nih.gov/pubmed/37742706 http://dx.doi.org/10.1098/rsta.2023.0176 |
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author | Zhu, Shun-Peng De Jesus, Abílio M. P. Berto, Filippo Michopoulos, John G. Iacoviello, Francesco Wang, Qingyuan |
author_facet | Zhu, Shun-Peng De Jesus, Abílio M. P. Berto, Filippo Michopoulos, John G. Iacoviello, Francesco Wang, Qingyuan |
author_sort | Zhu, Shun-Peng |
collection | PubMed |
description | 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)'. |
format | Online Article Text |
id | pubmed-10518223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-105182232023-09-25 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 Philos Trans A Math Phys Eng Sci Preface 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)'. The Royal Society 2023-11-13 2023-09-25 /pmc/articles/PMC10518223/ /pubmed/37742706 http://dx.doi.org/10.1098/rsta.2023.0176 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Preface Zhu, Shun-Peng De Jesus, Abílio M. P. Berto, Filippo Michopoulos, John G. Iacoviello, Francesco Wang, Qingyuan Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title | Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title_full | Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title_fullStr | Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title_full_unstemmed | Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title_short | Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
title_sort | preface to the theme issue ‘physics-informed machine learning and its structural integrity applications' |
topic | Preface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518223/ https://www.ncbi.nlm.nih.gov/pubmed/37742706 http://dx.doi.org/10.1098/rsta.2023.0176 |
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