Virtual special issue on machine learning for protective engineering by material design

Machine learning plays an essential role in the protective engineering and material design fields, and it promotes the rapid development of research. The Materials Genome Initiative (MGI), and the NSF's Harnessing the Data Revolution (HDR) Big Idea, are the US federal programs that provide incentive, attention, momentum and support to power these advances and drive the field forward. Machine learning is being used to establish the implicit structure-performance relations for different material compositions, processing histories, microstructure and service conditions. These in turn are being used to develop new materials with exceptional performance, by rapidly exploring the yet unknown property space of high dimensions. As engineering data is characterised in high dimension, small sample sizes, loud noise, missing values and uneven distributions, yield for the established machine learning models low prediction accuracy, poor generalisation ability and great uncertainty. Generally, an active learning strategy can be used to effectively improve the prediction accuracy of the model through experimental verification and data feedback iteration.

This special issue aims to capture the latest progress and trends in the application of machine learning to Materials Science, Engineering and Design under extreme loading conditions. We especially welcome contributions from researchers with engineering and materials backgrounds on the theory and the technology of machine learning in engineering applications and material design.

Topics covered:

Topics of interest include, but are not limited to:

• New computational models for material systems under extreme conditions
• Machine learning approaches to materials andstructure design
• Machine learning in ballistic-related case studies
• Machine learning in explosion-related case studies
• Machine learning and protective performance evaluation
• Machine learning for failure analysis and prediction

Important deadlines:

Submission deadline: 30 August 2022

Submission instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select VSI: Machine learning for protective engineering on submission.

Guest Editors:

• Prof. MengqiYuan, Beijing Institute of Technology, China. Email: myuan@bit.edu.cn
• Prof. Shan Tang, Dalian University of Technology, China. Email: shantang@dlut.edu.cn
• Assis. Prof. Khalil I. Elkhodary, The American University in Cairo, Egypt. Email: khalile@aucegypt.edu
• Dr. Chunyang Huang, Beijing Institute of Technology Chongqing Innovation Center, China. Email: huang_chunyang@bit.edu.cn