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Computer Methods in Applied Mechanics and Engineering
Article . 2025 . Peer-reviewed
License: Elsevier TDM
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An efficient kernel learning-based constitutive model for cyclic plasticity in nonlinear finite element analysis

descriptionPublicationkeyboard_double_arrow_right Article 01 Mar 2025 English Publisher:Elsevier BVJournal:Computer Methods in Applied Mechanics and Engineering, volume 436, page 117,700 (issn: 0045-7825, Copyright policy )
Authors: Liao, Yue; Luo, Huan;

doi: 10.1016/j.cma.2024.117700

An efficient kernel learning-based constitutive model for cyclic plasticity in nonlinear finite element analysis

Abstract

zbMATH Open Web Interface contents unavailable due to conflicting licenses.

Keywords

Finite element methods applied to problems in solid mechanics, extrapolation capability, efficient kernel learning, cyclic plasticity, elastoplastic materials, data-driven constitutive models, Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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