From virtual clustering analysis to self-consistent clustering analysis: a mathematical study

DOI10.1007/s00466-018-1573-xzbMath1471.74079OpenAlexW2791175628WikidataQ113327257 ScholiaQ113327257MaRDI QIDQ1628754

Lei Zhang, Wing Kam Liu, Shao-Qiang Tang

Publication date: 5 December 2018

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00466-018-1573-x

zbMATH Keywords

convergencemachine learningnumerical homogenizationSaint-Venant principleLippmann-Schwinger equation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Numerical and other methods in solid mechanics (74S99) Homogenization, determination of effective properties in solid mechanics (74Q99)

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Cites Work

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• The reduced model multiscale method (R3M) for the nonlinear homogenization of hyperelastic media at finite strains
• Mathematical foundations of the immersed finite element method
• Nonuniform transformation field analysis
• Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
• Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials
• Multiscale Modeling and Simulation of Composite Materials and Structures
• Multiscale Methods