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Start Over Collection UNT Scholarly Works Degree Department Materials Science and Engineering Degree Department Mechanical Engineering Day 12th

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Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models (open access)

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

This article combines machine learning (ML), finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. It also provides a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.
Date: July 12, 2021
Creator: Ferdousi, Sanjida; Chen, Qiyi; Soltani, Mehrzad; Zhu, Jiadeng; Cao, Pengfei; Choi, Wonbong et al.
System: The UNT Digital Library