physics-informed machine learning and its structural integrity applications (Integrity Applications)
90
Structured Review
Integrity Applications
physics-informed machine learning and its structural integrity applications
Physics Informed Machine Learning And Its Structural Integrity Applications, supplied by Integrity Applications, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/physics-informed+machine+learning/physics+informed+machine+learning/pm37980935-6-18-26
Average 90 stars, based on 1 article reviews
Physics Informed Machine Learning And Its Structural Integrity Applications, supplied by Integrity Applications, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/physics-informed+machine+learning/physics+informed+machine+learning/pm37980935-6-18-26
Average 90 stars, based on 1 article reviews
physics-informed machine learning and its structural integrity applications - by Bioz Stars,
2026-09
90/100 stars
Images
Related Articles
other:Article Title: High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach. Article Snippet: Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare. c.6837541.. High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach Huan Yu1, Yanan Hu1, Guozheng Kang1, Xin Peng2, Bingqing Chen3 and Shengchuan Wu1,2 1School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu 611756, People’s Republic of China 2State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, People’s Republic of China 3Bejing Institute of Aeronautical Materials, Beijing 100095, People’s Republic of China Article Title: Physics-informed machine learning and its structural integrity applications: state of the art. Article Snippet: Authors for correspondence: Shun-Peng Zhu e-mails: zspeng2007@uestc.edu.cn; zhu.s.peng@gmail.com Qingyuan Wang e-mail: wangqy@scu.edu.cn Physics-informed machine learning and its structural integrity applications: state of the art Shun-Peng Zhu1, Lanyi Wang1, Changqi Luo1, José A. F. O. Correia2, Abílio M. P. De Jesus2, Filippo Berto3 and Qingyuan Wang4,5 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China 2INEGI and CONSTRUCT, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal 3Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Roma, Italy 4MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, People’s Republic of China 5Advanced Research Institute, Chengdu University, Chengdu 610106, People’s Republic of China |